Eng. Proc., 2023, ITISE 2023

The ITISE 2023 (9th International Conference on Time Series and Forecasting) sought to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for inter- disciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics, forecaster, econometric, etc [...] Full article

The aim of this study is to develop the inventory planning system of a Portuguese tyre retailer based on forecasting sales models. Using sales history up to 2020, tyres were grouped into three levels of sales aggregation and different quantitative forecasting models were applied. The comparison of these models resorted to various evaluation measures to choose the most suitable one for each group. The study shows that for items with sales grouped monthly and for items with sales grouped by semester, Holt’s method had a better performance on determining sales forecasts, while for tyres with sales grouped quarterly, it was Croston’s method that stood out. The inventory policy outlined for each group of items reflects the results of the forecasted demand, and the review period depends on the sales group under analysis. In agreement with previous studies, the usefulness of statistical methods is corroborated. Additionally, the advantage of combining the said methods proved helpful, particularly as a starting point for tyre retail inventory planning. Full article

This article presents a Johansen test assessing the predetermined long-term relationships of datasets from a photovoltaic (PV) plant to predict the power output of an island zone. The goal was to use Johansen’s model to predict the PV power generation in the island of Mauritius. In this article, time series using an on-site measurement dataset have been used to design an original prediction model, the Johansen model for PV power output. This model is trained to predict random monthly, weekly, and daily PV power outputs in different seasons and years. The experimental results demonstrate that the Johansen model is a powerful medium-term predicting tool. Full article

Diphtheria is an infectious disease with a high mortality rate. In Indonesia, the number of diphtheria cases has remained relatively high in recent years, so efforts to prevent and control diphtheria are needed. Forecasting of the number of diphtheria cases was carried out in this study by applying a type-2 fuzzy logic systems method. Forecasting in this study was carried out by involving the variables of the number of diphtheria sufferers, the percentage of immunization coverage comprising four immunization types, and population density. Regions are grouped into three clusters based on the number of cases that have occurred. Each cluster is taken and sampled in the form of one region to acquire a robust model for other regions. The forecasting results for the next 24 periods show that the performance of the type-2 fuzzy logic systems method is quite good, with accuracy values in the Malang area showing an MSE of 8.785 and an SMAPE of 54.91%. In the Surabaya area, the forecasting accuracy results have an MSE value of 14.940 and an SMAPE of 35.51%. In the Sumenep area, the forecasting accuracy results show an MSE value of 2.188 and an SMAPE of 67.63%. The results of the forecasting of the number of cases can be used as a guide in planning and making decisions regarding the prevention and management of diphtheria. Full article

Triangular fuzzy numbers (TFNs) are used to express the weights of criteria and alternatives to account for the ambiguity and uncertainty inherent to subjective evaluations. However, the proposed method can easily be extended to other fuzzy settings depending on the uncertainty facing managers and decision-makers. Triangular fuzzy number (TFN) is a critical component in building fuzzy models such as fuzzy regression and fuzzy autoregressive. Many symmetrical triangular fuzzy numbers have been proposed to improve the scale’s linguistic accuracy. Additionally, Sturges’ rule is a well-known approach to determining criteria or intervals of grouped data. However, some existing TFN methods are challenging despite being considered in building fuzzy regression models. The increase in electricity distribution is caused by the number of customers and the amount of installed capacity factors in Indonesia. The identified factors are uncertainty, inexactness, and random nature. This paper investigates the residential electricity distribution model using fuzzy regression time series. In the beginning step, the integration between conventional TFN and Sturges’ rule was proposed to determine the criteria or scale of linguistic terms. The secondary data was collected from BPS Indonesia from 2000 to 2021. The dependent variable was denoted as electric power distribution $(Y_{RT})$. On the other hand, the number of customers and the amount of installed capacity were grouped as independent variables ($X_{PL}$ and $X_{KT}$). The results showed that the best forecasting model is an FLR right upper limit without constant. This proposed model also has higher MAPE accuracy at 1.44% compared to classical models. Additionally, the proposed triangular fuzzy number could improve the accuracy of the proposed model significantly. Interestingly, both dependent and independent factors were initially forecasted using a basic time series model, namely exponential smoothing. Full article

El Niño-Southern Oscillation (ENSO), a natural phenomenon in the Pacific Ocean, is caused by cyclic changes in sea-surface temperature (SST) and the overlying atmosphere in the tropical Pacific. The impact of ENSO varies, ranging from slightly warmer or colder temperatures to extreme weather events such as flash floods, droughts, and hurricanes, affecting various regions around the globe. Therefore, ENSO forecasting has paramount importance in the atmospheric and oceanic sciences. The Oceanic Niño Index (ONI), a three-month running mean of SST anomalies over the east–central equatorial Pacific region, is the commonly used metric for measuring ENSO events. However, the literature shows that the forecasting accuracy of ONI for lead times exceeding one year is low. This study aims to improve the forecast accuracy of ONI for up to 18 months lead time by applying an Adaptive Graph Convolutional Recurrent Neural Network (AGCRNN). The graph-learning module adaptively learns the spatial structure of features during training, while the graph convolution in hidden layers of the recurrent neural network captures the temporal relationships of features with ONI. Experiments conducted on simulation and reanalysis datasets demonstrate that AGCRNN outperforms state-of-art statistical and eight dynamical models for forecasting ONI with up to 18 months’ lead time. Full article

The study proposes an ensemble spatiotemporal methodology for short-term rainfall forecasting using several data mining techniques. Initially, Spatial Kriging and CNN methods were employed to generate two spatial predictor variables. The three days prior values of these two predictors and of other selected weather-related variables were fed into six cost-sensitive classification models, SVM, Naïve Bayes, MLP, LSTM, Logistic Regression, and Random Forest, to forecast rainfall occurrence. The outperformed models, SVM, Logistic Regression, Random Forest, and LSTM, were extracted to apply Synthetic Minority Oversampling Technique to further address the class imbalance problem. The Random Forest method showed the highest test accuracy of 0.87 and the highest precision, recall and an F1-score of 0.88. Full article

In Brazil, Law n. 9394/96 ensures rights such as the identification of talented students in order to offer specialized educational attention; in this sense, the Protocol for Screening of Musical Abilities was elaborated with 54 items and its complementary instruments (scales and questionnaires) in order to collaborate with the survey of students with indicators of musical talent. This work, therefore, aims to present the instruments and the evidence of effectiveness and usability found in a preliminarily manner. It is an investigation in progress and incorporates experimental psychometric (elaboration of scales) and psycho-physical (peer comparison method) methods. In total, 800 individuals, including children from six to eleven years old, their guardians (family members) and teachers, took part in the research. The results have indicated that participants with higher scores remain with the same indices in the later stage of evaluation; however, only from the statistical tests intended for validation, standardization and reliability, as well as exploratory factor analysis will it be possible to attest the validity, standardization of scores and prepare the final version for wide use of the instruments. Full article

As the global environmental crisis grows in scale and complexity, land protection experts and policy makers are increasingly called upon to make decisions, despite high levels of uncertainty, limited resources and insufficient or, conversely, available but unintegrated data. Efforts to protect biodiversity at the national and, especially, the local level, which aim to achieve sustainable development in territories and local communities, require the incorporation of social, economic and political considerations to ensure that participatory planning of strategies is adopted and undertaken. With this issue in mind, the geographical focus chosen for this contribution is the territory of the Valley of Roses located in the southern area of Morocco. From a methodological point of view, this paper will address the state of the existing literature on sustainable development and the good practices implemented in studied territories. The final objective, which is related to the application and resolution of real problems, concerns, on the one hand, the possibility of valorizing the material and immaterial cultural heritage of the area and, on the other hand, identifying the steps to be taken as part of a long-term vision aimed at identifying concrete actions for the valorization and development of the area. Full article

In this paper, we use the time-frequency wavelet estimators to analyze the robustness of Okun’s Law in the European Union across time and within various economic cycles. We extend the Okun’s Law literature as we focus on Europe, directly estimating the time-frequency varying Okun’s coefficient. We observe that Okun’s coefficient in Europe is unstable at short run (infra and annual cycles). The strength of Okun’s Law is time dependent at short run as linkages between growth and unemployment are stronger only during crisis times. Such instability is explained as unemployment predominates growth, leading to a positive coefficient and weaker strength. However, as the frequencies increase, the coefficient is more stable over time and the strength is higher and homogenous over time. Full article

El Niño-Southern Oscillation (ENSO) is caused by periodic fluctuations in sea surface temperature and overlying air pressure across the Equatorial Pacific region. ENSO has a global impact on weather patterns and can cause severe weather events, such as heat waves, floods, and droughts, affecting regions far beyond the tropics. Therefore, forecasting ENSO with longer lead times is of great importance. This study utilizes Long Short-Term Memory (LSTM) network to predict ENSO events in the coming year based on environmental variables from previous years, including sea-surface temperature, sea level pressure, zonal wind, meridional wind, and zonal wind flux. These environmental variables are collected only inside certain spatial and temporal windows and used to forecast ENSO events. Furthermore, this study investigates how the size of these spatial and temporal windows influences the generalization accuracy of forecasting ENSO events. The size of spatial and temporal windows is optimized based on the generalization accuracy of the LSTM network in forecasting ENSO events. Our results indicated that the accuracy of the ENSO forecast is significantly sensitive to the extent of spatial and temporal windows. Specifically, increasing the temporal window size from one to nine years and the spatial window from 0 to 17.7 geographical degrees resulted in generalization accuracies, ranging from 40.1% to 83% in forecasting Central Pacific ENSO and 39.2% to 65% in forecasting Eastern Pacific ENSO. Full article

Accurate precipitation forecasting is essential for emergency management, aviation, and marine agencies to prepare for potential weather impacts. However, traditional radar echo extrapolation has limitations in capturing sudden weather changes caused by convective systems. Deep learning models, an alternative to radar echo extrapolation, have shown promise in precipitation nowcasting. However, the quality of the forecasted radar images deteriorates as the forecast lead time increases due to mean absolute error (MAE, a.k.a L1) or mean squared error (MSE, a.k.a L2), which do not consider the perceptual quality of the image, such as the sharpness of the edges, texture, and contrast. To improve the quality of the forecasted radar images, we propose using the Structural Similarity (SSIM) metric as a regularization term for the Conditional Generative Adversarial Network (CGAN) objective function. Our experiments on satellite images over the region 83° W–76.5° W and 33° S–40° S in 2020 show that the CGAN model trained with both L1 and SSIM regularization outperforms CGAN models trained with only L1, L2, or SSIM regularizations alone. Moreover, the forecast accuracy of CGAN is compared with other state-of-the-art models, such as U-Net and Persistence. Persistence assumes that rainfall remains constant for the next few hours, resulting in higher forecast accuracies for shorter lead times (i.e., <2 h) measured by the critical success index (CSI), probability of detection (POD), and Heidtke skill score (HSS). In contrast, CGAN trained with L1 and SSIM regularization achieves higher CSI, POD, and HSS for lead times greater than 2 h and higher SSIM for all lead times. Full article

The forecasting of a signal that locally satisfies linear recurrence relations (LRRs) with slowly changing coefficients is considered. A method that estimates the local LRRs using the subspace-based method, predicts their coefficients and constructs a forecast using the LRR with the predicted coefficients is proposed. This method is implemented for time series that have the form of a noisy sum of sine waves with modulated frequencies. Linear and sinusoidal frequency modulations are considered. The application of the algorithm is demonstrated with numerical examples. Full article

Traffic incidents usually have negative effects on transportation systems such as delays and traffic jams. Therefore, a traffic incident response plan can guide management actors and operators to take action effectively and timely after traffic incidents. In this paper, an approach has been proposed to generate and evaluate traffic incident response plans automatically when a traffic incident is detected. In this approach, a library of response action templates has been constructed beforehand to be used in the real-time generation process of a response plan template. According to the type and severity of the detected and confirmed traffic incident, a combination of relevant response action templates will provide a set of response plans. In addition, we have developed a simulation model for the study area by using Aimsun Next software (version 20.0.3), developed by Aimsun, to evaluate the performance of the generated response plans. Therefore, the simulation outcomes determine the rank of the generated response plans including the optimal response plans. The proposed approach considers the characteristics of input traffic incidents and transport road networks to generate response plans. Furthermore, the choice of the optimal response plan considers the characteristics of the input traffic incident. The implementation results show that the generated response plans can enhance and improve the overall network performance and conditions efficiently. In addition, the response plan ranking is considered to be a supportive tool in the network operators’ decision-making process in terms of the optimal response plan to be implemented or propagated. Full article

The purpose of this paper is to identify econometric models likely to highlight the impact of the COVID-19 pandemic on the financial markets. The Markov-switching “GARCH and EGARCH” models are suitable for analyzing and forecasting the series of daily returns of the major global stock indices (i.e., SSE, S&P500, FTSE100, DAX, CAC40, and NIKKEI225) during the pre-COVID-19 period, from 1 June to 30 November 2019, and the post-COVID-19 period, from 31 December 2019, to 1 June 2020. The Markov-switching “GARCH and EGARCH” models allow good modeling of the conditional variance. The estimated conditional variance values by these models highlight the increase in volatility for the stock markets in our sample, during the post-COVID-19 period compared to that pre-COVID-19, with a peak in volatility in “early January 2020” for the Chinese stock market and in “March 2020” for the other five stock markets (i.e., New York, Paris, Frankfurt, London, and Tokyo). The stock exchange of Frankfurt has shown great resilience compared to other international stock exchanges (i.e., the stock exchanges in Paris, London, and New York). The modeling of the impact of the COVID-19 pandemic on the financial markets by the Markov-switching “GARCH and EGARCH” models makes it possible to simultaneously take into consideration the nonlinearity at the level of the mean and the variance, and to obtain the results of the transition probabilities, the unconditional probabilities and the conditional anticipated durations during the pre-COVID-19 period and the post-COVID-19 period. Full article

This paper proposes a refinement of the multivariate diffusion UCTT model to explore the energy transition in the US commercial sector. The model analyzes the electricity market’s interdependencies between coal, gas, and biomass, allowing a deeper understanding of the system. In addition, the comparison with the industrial sector electric system provides a valuable indication of how the US commercial sector has solid grounds for a more ready and suitable environment to accelerate the energy transition. Full article

The accurate long-term forecasting of solar activity is crucial in the current era of space explorations and in the study of planetary climate evolution. With timescales of about 11 years, these forecasts deal with the prediction of the very general features of a solar cycle such as its amplitude, peak time and period. Solar radio indices, continuously measured by a network of ground-based solar radio telescopes, are among the most commonly used descriptors to characterise the solar activity level. They can act as proxies for the strength of ionising radiations, such as solar ultraviolet and X-ray emissions, which directly affect the atmospheric density. In a preliminary comparative study of a selection of univariate deep-learning methods targeting medium-term forecasts of the F10.7 index, we noticed that the performance of all the considered models tends to degrade with increasing timescales and that this effect is smoother when a multi-attention module is included in the used neural network architecture. In this work, we present a multivariate approach based on the combination of fast iterative filtering (FIF) algorithm, long-short term memory (LSTM) network and multi-attention module, trained for the present solar cycle forecasting. Several solar radio flux time series, namely F3.2, F8, F10.7, F15, F30, are fed into the neural network to forecast the F10.7 index. The results are compared with the official solar cycle forecasting released by the Solar Cycle Prediction Panel representing NOAA, NASA and the International Space Environmental Services (ISES) to highlight possible discrepancies. Full article

The concept of risk assessment is an important tool in the asset integrity management of power distribution systems. This manuscript presents a risk-based asset integrity management (RBAIM) methodology for the optimization of power distribution assets using a time series analysis approach. This approach deals with time series forecasting on risk assessment for low-voltage-level (400/230 V) failures using the Python programming language and considering historical low-voltage (LV) fuse failure data from a case study over 44 months, starting from 2019. The proposed approach is deployed in a power distribution utility located in a densely populated area of Colombo district, Sri Lanka. The authors proposed a methodical approach for the identification of priority components for asset maintenance and repair ranking based on the risk index percentage value to enhance the predictiveness of potential defects and estimate the risk of potential failures. The results show that the proposed time series forecasting methodology for RBAIM is useful for power distribution utility asset owner organizations for continuous monitoring, the evaluation of asset conditions, and the implementation of proper maintenance and repair strategies to enable assets to perform at their optimal level. The proposed RBAIM methodology enables practicing engineers to assure the asset integrity of power distribution utilities. Full article

Forecasting imbalance volumes are of great importance for the different actors in electricity markets. From a transmission system operator (TSO) perspective, balancing supply and demand in real-time is one of the main operational tasks to ensure the safe and reliable operation of the power system, while market participants also use forecasting tools to enhance their participation strategy in electricity wholesale markets. Over the last few years, the increasing integration of renewable energy sources into the power system has created additional complexity for the problem of accurately determining the imbalance volume. In the present work, a case study of the Greek balancing market is presented and analysed. Different algorithms and a set of external predictors are adopted both from the market and operational perspective and compared for two different forecasting horizons. Full article

Energy is the key driver of economic growth; however, the economic leadership position of G7 countries and the rising global manufacturing hub status of the ASEAN-5 countries have yet to achieve the Sustainable Development Goals. Thus, this paper aims to examine the effects of real GDP per capita, urban population, the number of individuals using the internet, carbon dioxide emissions, total trade and net foreign direct investment (FDI) inflows on the renewable energy consumption (REC) of G7 and ASEAN-5 countries from 1990 to 2021 yearly data. Using Studenmund’s and Gujarati and Porter’s procedures of the panel data model, the panel fixed-effect econometric modelling held the best outcome for both G7’s and ASEAN-5 countries’ REC models. Based on the findings, urban population highly and positively affects REC in G7 countries. However, there is also a positive and strong relationship between net FDI inflows and REC in ASEAN-5 countries. The empirical findings prove the importance of macroeconomic, socioeconomic and environmental variables for the outcomes of REC policies across both developed and developing countries. Full article

This paper presents a study on the creation of a tool to help powerlifting athletes and coaches, as well as bodybuilders and other amateur gym athletes, to analyse their data and obtain useful information regarding the athlete’s performance. The tool should also predict future personal records in lifting for both raw (non-equipped) and non-raw (equipped) attempts, and their various exercises. In order to achieve this, a dataset with entries of around 500 k lifters and more than 20 k official powerlifting competitions was used. Among those entries, biometric variables of the lifters and the weights they lift in each of the three movements of this sport discipline were included: squat, bench press, and deadlift. We applied data preprocessing and visualising as well as data splitting and scaling techniques in order to train the machine learning models that are used to make the predictions. Lastly, the best predictive models were used in the implemented tool. Full article

Landslides are nonstationary and nonlinear phenomena, which are often recorded as high-dimensional vector time series manifesting spatiotemporal dependence. Contemporary econometric methods use error-correction cointegration (ECC) and vector autoregression (VAR) to handle the nonstationarity but ignore the nonlinear trend. Here, we improve the ECC-VAR methodology by inserting a nonlinear trend $\mathit{c}\left(t\right)$ into the model and nonparametrically estimating it by penalised maximum likelihood, and name this method ECC-VAR-$\mathit{c}\left(t\right)$. Assisted by the empirical dynamic quantiles (EDQ) dimension reduction technique, it is sufficient to apply ECC-VAR-$\mathit{c}\left(t\right)$ to just a small number of representative EDQ series to surmise the whole dataset. The application of this ECC-VAR-$\mathit{c}\left(t\right)$ is well fitted to the real-world slope dataset ($R^2=0.99$) that consists of 1803 time series, each having 5090 time states. In addition to the forecast values, we also provide three risk assessments to predict locations, time and risk of a future failure with quantified uncertainty for building an early-warning system (e.g., predicted time of failure (ToF), where the minimum error is 2.7 h before the actual ToF). Full article

Floods are repetitive and unpredictable phenomena. Interventions in these cases are carried out during or after a hazard’s occurrence. The quality and density of the roads, the equipment of emergency intervention centers, and the proximity of these centers to risk areas influence accessibility and intervention capacity. An analysis of this issue is carried out in the present article for a small catchment in Romania in a predominantly hilly area. Areas with very high and very low road accessibility are identified. Proximity to emergency centers can reduce travel time, thus increasing the surfaces of well-served areas. Full article

In Italy, most of the destructive landslides are triggered by rainfall, particularly in central Italy. Therefore, effective monitoring of rainfall is crucial in hazard management and ecosystem assessment. Global precipitation measurement (GPM) is the next-generation satellite mission, which provides the precipitation measurements worldwide. In this research, we employed the available monthly GPM data to estimate the monthly precipitation for the twenty administrative regions of Italy from June 2000 to June 2021. For each region, we applied the non-parametric Mann–Kendall test and its associated Sen’s slope to estimate the precipitation trend for each calendar month. In addition, for each region, we estimated a linear trend and the seasonal cycles of precipitation with the antileakage least-squares spectral analysis (ALLSSA) and showed the annual precipitation variations using box plots. Lastly, we compared machine-learning models based on the auto-regressive moving average for monthly precipitation forecasting and showed that ALLSSA outperformed them. The findings of this research provide a significant insight into processing climate data, both in terms of trend-season estimates and forecasting, and can potentially be used in landslide susceptibility analysis. Full article

In this article, we present the use of risk terrain modeling for the risk assessment of explosive ATM attacks in North Rhine-Westphalia, Germany. We give a brief overview of three methods used for this purpose: risk terrain modeling, recapture rate index, and time series analysis. The results show that by using these methods, police can gain a deeper understanding of the patterns and trends associated with explosive ATM attacks and better allocate their resources by focusing on higher-risk ATMs. Full article

The region defined by the South Shetland Islands, Bransfield Sea, and Antarctic Peninsula has complex geodynamic activity resulting from the active subduction process of the Phoenix Plate under the Antarctic Plate. This subduction produces a rift of expansion along the Bransfield Sea basin between the South Shetland Islands and the Antarctic Peninsula. There is also a chain of submarine (Orca, Three Sisters, and Building A) and emerged (Deception and Pinguin) volcanoes. In 2020, there was intense seismic activity around the Orca volcano with earthquakes of up to 6.9 Mw. This paper presents displacement models of this seismic activity produced in the region. The geodetic time series of the GNSS stations located in the region were analyzed: UYBA at the Uruguayan Artigas Antarctic Base (King George Island) and PAL2 at the U.S. Palmer Antarctic Base (Anvers Island). These data were taken from the Nevada Geodetic Laboratory. Full article

The availability of solar irradiance time series without missing data is an ideal scenario for researchers in the field. However, it is not achievable for a variety of reasons, such as measurement errors, sampling gaps, or other factors. Time series imputation methods can be a solution to the lack of data and, in this paper, we study the applicability of Bidirectional Encoder Representations from Transformers (BERT) as an irradiance time series imputation solution. In this regard, a BERT model was trained from scratch for the masked language modelling (MLM) task, and the quality of the imputation was evaluated according to the number of missing values and the position within the series. The experiments were conducted over a dataset of 165 stations, captured by meteorological stations distributed over the Spanish regions of Galicia, Castile, and León. In the evaluation process, an average coefficient of determination (R² score) of 0.89% was obtained, the maximum result being 0.95%. Full article

Bitcoin is one of the cryptocurrencies that has gained popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast its future level, and other price-related features are necessary to improve forecast accuracy. We introduce a new set of time series and demonstrate that a subset is necessary to improve directional accuracy based on a machine learning ensemble. In our experiments, we study which time series and machine learning algorithms deliver the best results. We found that the most relevant time series that contribute to improving directional accuracy are open, high, and low, with the largest contribution of low in combination with an ensemble of a gated recurrent unit network and a baseline forecast. The relevance of other Bitcoin-related features that are not price-related is negligible. The proposed method delivers similar performance to the state of the art when observing directional accuracy. Full article

This work presents dynamic Tikhonov state forecasting based on large-scale deep neural network constraint for the solution to a dynamic inverse problem of electroencephalographic brain mapping. The dynamic constraint is obtained by using a large-scale deep neural network to approximate the dynamics of the state evolution in a discrete large-scale state-space model. An evaluation by using neural networks with several hidden layer configurations is performed to obtain the adequate structure for large-scale system dynamic tracking. The proposed approach is evaluated over two models of 2004 and 10,016 states in discrete time. The models are related to an electroencephalographic problem for EEG generation. A comparison analysis is performed by using static and dynamic Tikhonov approaches with simplified dynamic constraints. By considering the obtained results it can be concluded that the deep neural networks adequately approximate large-scale state dynamics by improving the dynamic inverse problem solutions. Full article

Functional data analysis has demonstrated significant success in time series analysis. In recent biomedical research, it has also been used to analyze sequence variations in genome-wide association studies (GWAS). The observations of genetic variants, called single-nucleotide polymorphisms (SNPs), of an individual are distributed over the loci of a DNA sequence. Thus, it can be regarded as a realization of a stochastic process, which is no different from a time series. However, SNPs are usually coded as the number of minor alleles, which are categorical. The usual least-square smoothing in FDA only works well when the data is continuous and normally distributed. The normality assumption will be violated for categorical SNP data. In this work, we propose a two-step method for smoothing categorical SNPs using a novel method and constructing haplotypes having strong associations with the disease using functional generalized linear models. We show its effectiveness through a real-world PennCATH dataset. Full article

Identifying meaningful and actionable relationships between the price movements of financial assets is a challenging but important problem for many financial tasks, from portfolio optimization to sector classification. However, recent machine learning research often focuses on price forecasting, neglecting the understanding and modelling of asset relationships. To address this, we propose a neural model for training stock embeddings that harnesses the dynamics of historical returns data to reveal the nuanced correlations between financial assets. We describe our approach in detail and discuss several practical ways it can be used in the financial domain. Specifically, we present evaluation results to demonstrate the utility of this approach, compared to several benchmarks, in both portfolio optimization and industry classification. Full article

We propose a novel approach to cluster hierarchical time series (HTS) for efficient forecasting and data analysis. Inspired by a practically important but unstudied problem, we found that leveraging local information when clustering HTS leads to a better performance. The clustering procedure we proposed can cope with massive HTS with arbitrary lengths and structures. In addition to providing better insights, this method can also speed up the forecasting process for a large number of HTS. Each time series is first assigned the forecast from its cluster representative, which can be considered as “prior shrinkage” for the set of time series it represents. Then, the base forecast can be efficiently adjusted to accommodate the specific attributes of the time series. We empirically show that our method substantially improves performance for large-scale clustering and forecasting tasks involving HTS. Full article

In recent decades, Artificial Intelligence (AI) has become an essential tool for modeling and forecasting in different research fields. Mechanical engineering is no exception because practical problems that classical methods can hardly solve can receive more efficient solutions using AI. Given a support scheme of a structural system, the article aims to determine the maximum stiffness of the system based on the series of moments’ variation for a variable dimensional parameter of the support. The series represents the input for a Gene Expression Programming (GEP) aiming to determine the model for a specific geometric parameter in mechanical structures, namely, deflection. Full article

In forecasting socio-economic processes, it is essential to have tools that are highly performing, with results as close to reality as possible. Forecasting plays an important role in shaping the decisions of governments and central banks about macroeconomic planning, and it is an essential analytical tool in defining economic strategies of countries. The most common forecasting methods used in the analysis of macroeconomic processes are based on extrapolation, i.e., extending the trend observed in the past (and present) to the future. However, the presence of non-linearity in the socio-economic systems under uncertainty, as well as the partial observability of the processes, has contributed to make researchers and practitioners consider other methodologies, too. In this paper, we analyze 18 time series of macroeconomic variables of the United States of America. We compare the benchmark results obtained with “classic” forecasting techniques with those obtained with our proposed architecture. The model we construct can be defined as “hybrid” since it combines a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM) backend. We show that, for what concerns minimizing the forecast error, our model competes with and often improves the results obtained with the benchmark techniques. The goal of this work is to highlight that, due to the recent advances in computing power, new techniques can be added to the set of tools available to a policymaker for forecasting macroeconomic data. Full article

We propose a novel model to achieve superior out-of-sample Sharpe ratios. While most research in asset allocation focuses on estimating the return vector and covariance matrix, the first component of our novel model instead forecasts the future tangency portfolio, and the second component then determines the optimal investment portfolio. First, to forecast the tangency portfolio, we forecast the efficient frontier by decomposing its functional form, a square root second-order polynomial, into three interpretable coefficients, which can then be used to calculate a forecasted tangency portfolio. These coefficients can be forecasted using vector autoregressions. Second, the model invests in the portfolio on the efficient frontier that is the minimum Euclidean distance from this forecasted tangency portfolio. A motivation for our approach is to address the limitation that the tangency portfolio only maximizes the Sharpe ratio when future returns and covariances are stationary, and can be directly estimated with historical data, which often does not hold in out-of-sample data. Our approach addresses this shortcoming in a novel way by forecasting the tangency portfolio, rather than estimating return and covariance. For empirical testing, we employ two sets of assets that span the market to demonstrate and validate the performance of this novel method. Full article

In this article, we develop the Nerlove models that give the area of cacao and cashew nuts in terms of the area, the price and the rainfall. These models are estimated using the methods of ordinary least square and likelihood maximum and are used to analyze the link in a short time between the agricultural determinants. The results showed that the anticipation elasticity had an effect on the price practiced and forecast model. In a short time, the price delayed by one year, and the area delayed by one and two years, had decreasing returns to scale for the current area. Full article

Most real time series exhibit certain characteristics that make the choice of model and its specification difficult. The objective of this study is to address the problem of parameter estimation and the accuracy of forecasts k-steps ahead in non-stationary time series with outliers in the context of state-space models. In this paper, three methods for detecting and treating outliers are proposed. We also present a comparative study of the proposed methods using data simulated from a local level model with sample sizes of 50 and 500 and with various combinations of parameters, with a 5% contamination error rate of the observation equation. The results were evaluated in terms of the accuracy of model parameters and the forecasts k-steps ahead, as well as the detection rate of true outliers. These methodologies are applied to three real examples. This study shows that the local level model is sufficiently robust even for non-stationary contaminated series, in the sense that they are able to handle non-stationary time series and outliers in a satisfactory way. Full article

The challenges confronting management in making decisions on offshore outsourcing have inevitably changed dramatically over the last decade. Offshore outsourcing today is characterized by a higher level of complexity and disruption brought about mainly factors of change, as: (1) the new waves of globalization; (2) the rapidly shifting conditions in the marketplace, which have made offshoring a vital part of global strategies, leading the way to new business models; (3) the increasing variety of models in the global supply chain; (4) the inroads of previous providers that have emerged as world-class competitors; and (5) the irresistible march of technological disruption. Threatening events, as terrorist attacks and wars, had disrupted the world economy since the 2000s. In these cases, supply chains were disrupted although soon restored. After these critical events, globalization was severely weakened by the arrival of the COVID-19 pandemic, on the threshold of 2020, and Russia’s invasion of Ukraine dealt a blow to global supply chains, by 2022. Due to these complexities, the trend to outsource production to other countries might increasingly push companies to transform themselves into virtual organizations. Outsourcing strategies could therefore evolve moving towards a digital era. Full article

In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rainfall, and temperature. To achieve our objective, we compare the performance of three statistical models, namely the Vector Auto-Regressive (VAR) model, the Vector Error Correction model (VECM), and the Generalized Linear model (GLM), using different metrics. The modeling framework will be compared with the Markov Switching (MSM) approach. Full article

In today’s world of large volumes of data, where the usual statistical estimation methods are commonly inefficient or, more often, impossible to use, aggregation methodologies have emerged as a solution for statistical inference. This work proposes a novel procedure for time series regression modelling, in which maximum entropy and information theory play central roles in the replication of time series, estimation of parameters, and aggregation of estimates. The preliminary results reveal that this three-stage maximum entropy approach is a promising procedure for time series regression modelling in big data contexts. Full article

Despite being the second largest oil exporter in Africa, Angola continues to lag behind in most macroeconomic and institutional indicators. At least partially, this is a consequence of the Dutch disease, a phenomenon that establishes a clear link between high resource endowments and lack of economic diversity through the loss of international competitiveness in non-resource sectors. In this paper, we use a nonlinear autoregressive distributed lag (NARDL) model to identify the cointegrated relationship between international oil prices and the real effective exchange rate of the kwanza, which is a striking sign of the presence of the Dutch disease. Full article

Weather forecasts are indispensable for the decision on the direction of operation of a runway system. Since the forecasts contain uncertainties, additional challenges arise for runway configuration management (RCM). With developments in machine learning, numerous models have been developed to improve forecasts and assist management. In this contribution, an intrinsically explainable Self-Enforcing Network (SEN) is presented as a decision support system for the RCM at Frankfurt Airport. Full article

This study extends previous work applying unsupervised machine learning to commodity markets. The first article in this sequence examined returns and volatility in commodity markets. The clustering of these time series supported the conventional ontology of commodity markets for precious metals, base metals, agricultural commodities, and crude oil and refined fuels. A second article used temporal clustering to identify critical periods in the trading of crude oil, gasoline, and diesel. This study combines the ontological clustering of financial time series with the temporal clustering of the matrix transpose. Ontological clustering, contingent upon the identification of structural breaks and other critical periods within financial time series, is this study’s distinctive contribution. Conditional, time-variant ontological clustering should be applicable to any set of related time series, in finance and beyond. Full article

The power sector is a major contributor to anthropogenic global warming and is responsible for 38% of total energy-related carbon dioxide emissions and 66% of carbon dioxide emission growth in 2018. In OECD member countries, the residential sector consumes a significant amount of electrical energy, with household refrigerating appliances alone accounting for 30–40% of the total consumption. To analyze the energy use of each domestic appliance, researchers have developed Appliance-Level Energy Characterization (ALEC), a set of techniques that provide insights into individual energy consumption patterns. This study proposes a novel methodology that utilizes robust probability density estimation to detect refrigerators with high energy consumption and recommend tailored energy-saving measures. The methodology considers two consumption features: base energy consumption (energy usage without human interaction) and relative energy consumption (energy usage influenced by human interaction). To assess the approach’s effectiveness, the methodology was tested on a dataset of 30 different appliances from monitored homes, yielding positive results that support the robustness of the proposed method. Full article

The modeling of hydrogeological processes often involves a quantitative description of complex systems in which a limited dataset is available, bringing about the formulation of conceptual models able to describe them in a simplified framework. In order to evaluate the reliability of these conceptual models, a statistical description of the elements composing the system can be useful, especially with reference to their mutual interactions. This study shows, through some applicative examples in the hydrogeological field, that the statistical analysis of characterizing the parameters and cause–effect relations arising from time series monitoring data can give useful information about the system dynamic, thus contributing to updating the conceptual model and therefore improving the results of following numerical modeling. Indeed, this dynamic description of the system, with the introduction of the verification and validation processes of the conceptual model, allows the correction of possible errors due to a lack of data or the phenomenon’s complexity. This leads to many hydrogeological issues, such as the identification of the most productive aquifer or the one that has the highest vulnerability to pollution, as well as zones of interest in groundwater flow that can trigger slope instability. Full article

Nonstationary is one of the prominent phenomena in the current hydrological time series due to climate change and urban expansion. In this study, using the long time series rainfall data from rain gauges and satellite rainfall data, the trend and abrupt change of rainfall in the Taihu Lake basin, China, are examined by the Mann–Kendall (MK) test and the Pettitt test, using rain gague data. For seven water conservancy zones in this basin, the intensity–duration–frequency curves (IDFs) are obtained using satellite rainfall and the stochastic storm transposition (SST) method, providing a method for rainfall frequency analysis based on nonstationary assumption. The IDFs results between the conventional frequency analysis method with the stationary assumption and the SST-based method are compared. The results show an overall increasing trend of annual total rainfall in the Taihu Lake Basin, with significant changes at most stations. The SST-based results show a significant difference of IDFs in seven conservancy zones, which are linked to nonstationary changes in rainfall series. Our results provide an important reference for understanding the nonstationary changes and nonstationary frequency analysis of extreme rainfall in the Taihu Lake basin. Full article

The paper deals with privatization processes in five selected countries of the communist regime and their comparative analysis. Most countries have historically encountered the need to privatize state-owned enterprises. A closed and inefficient economic system would reach the brink of resilience and change was necessary. Privatization was a conceptual solution. Due to diversified economic systems, internal social and political differences, as well as the complexity of the privatization process itself, the ways in which it has been implemented vary greatly from country to country. However, the aspiration has generally always meant overall economic improvement, and the implementation of rapid, formal, as painless as possible, preferably spontaneous, and transparent privatization. Still, everything took place in several stages and there was a mass, and most often coupon, privatization in one of the phases. It was concluded that each privatization process is specific, but there are still some overlaps. The main distinguishing criteria are related to the approach towards privatization (modular or inflexible) as well as centralization (Croatia, Czechoslovakia, and Poland) and decentralization (Slovenia and Hungary) of the system that implements and controls privatization. In addition, in some countries there has been a lack of public support due to numerous embezzlements, frauds and attempts to exploit positions of power at a given time, while on the other hand orderliness, legitimacy, and innovation have resulted in an in-flow of foreign capital and successful privatization supported by the public. Full article

This paper shows that we can use social media data to improve the accuracy of GDP estimates at the country level for developing countries. I use all publicly available image tweets from 2012 and 2013 to estimate GDP at the country level for developing countries. First, I find that one can explain 76% of the cross-country variation in GDP with the volume of tweets sent from each country. I then show that the residuals on these Twitter-GDP estimates are significantly larger for countries with allegedly poor data quality. I then use Nigeria as a case study to show that this method delivers much more timely and accurate estimates than those presented by official statistic agencies. Full article

The use of neuroimaging to predict individual and population-wide behaviors, also known as neuroforecasting, was long applied to estimate movie popularity. Only recently, EEG-based neural synchronization, which is indicative of engagement, was found as a valid predictor of the listening behavior of the population. However, the population’s evaluative responses to the songs were not incorporated. To fill this void, this study explored whether neural synchrony can also be related to likes, dislikes and comments for the same songs on YouTube more than two years after their release. In this way, we aimed to separate passive engagement (i.e., listening) from active engagement (evaluating). The results showed that neural synchrony was a significant predictor of the likes and comments on YouTube, even after controlling for explicit liking ratings from the lab study. In contrast, frontal alpha asymmetry did not predict YouTube likes. Thus, engagement as represented by neural synchronization could be a valuable tool for predicting active as well as passive engagement with entertainment products. This underlines the value of neural similarity in predicting the impact of music and videos before their true effect in the crowd can be known. Full article

During the first year of the COVID-19 pandemic, governments only had access to non-pharmaceutical interventions (NPIs) to mitigate the spread of the disease. Various methods have been discussed in the literature for calculating the effectiveness of NPIs. Among these methods, the interrupted time series analysis method is the area of our interest. To study the second wave, we clustered countries based on levels of implemented NPIs, except for the target NPI (X) whose effectiveness wanted to be evaluated. To do so, the COVID-19 Policy Response Tracker data-set gathered by the “Our World in Data” team of Oxford University, and COVID-19 statistical data gathered by the John Hopkins Hospital were used. After clustering, we selected a counterfactual country from the countries that were in the same cluster as the target country, and implemented NPI (X) at its lowest level. Thus, the target country and the counterfactual country were similar in implementation level of other NPIs and only differed in the implementation level of the target NPI (X). Therefore, we can calculate the effectiveness of NPI (X) without being concerned about the impurity of the effectiveness values that might be caused by other NPIs. This allowed us to calculate the effectiveness of NPI (X) using the interrupted time series analysis with the control group. Interrupted time series analysis assesses the effect of different policy-implementation levels by evaluating interruptions caused by policies in trend and level after the policy-implementation date. Before the NPI-implementation date, the implementation levels of NPIs were similar in both selected countries. After this date, the counterfactual country could be treated as a baseline for calculating changes in the trends and levels of COVID-19 cases in the target country. To demonstrate this approach, we used the generalized least square (GLS) method to estimate interrupted time series parameters related to the effectiveness of school closure (the target NPI) in Spain (the target country). The results show that increasing the implementation level of school closure caused a 34% decrease in COVID-19 prevalence in Spain after only 10 days compared to the counterfactual country. Full article

Modelling the non-stationary unconstrained bivariate integer-valued autoregressive of order 1 (NSUBINAR(1)) model is challenging due to the complex cross-correlation relationship between the counting series. Hence, this paper introduces a novel non-stationary unconstrained BINAR(1) with geometric marginals (NSUBINAR(1)GEOM) based on the assumption that the counting series are both influenced by the same time-dependent explanatory variables. The generalized quasi-likelihood (GQL) estimation method is used to estimate the regression and dependence parameters. Monte Carlo simulations and an application to a real-life accident series data are presented. Full article

In this paper, we propose a method for combining forecasts generated by different models based on long short-term memory (LSTM) ensemble learning. While typical approaches for combining forecasts involve simple averaging or linear combinations of individual forecasts, machine learning techniques enable more sophisticated methods of combining forecasts through meta-learning, leading to improved forecasting accuracy. LSTM’s recurrent architecture and internal states offer enhanced possibilities for combining forecasts by incorporating additional information from the recent past. We define various meta-learning variants for seasonal time series and evaluate the LSTM meta-learner on multiple forecasting problems, demonstrating its superior performance compared to simple averaging and linear regression. Full article

Moving object path prediction in traffic scenes from the perspective of a moving vehicle can improve safety on the road, which is the aim of Advanced Driver Assistance Systems (ADAS). However, this task still remains a challenge. Work has been carried out on the use of $x,y$ positional information of the moving objects only. However, besides positional information there is more information that surrounds a vehicle that can be leveraged in the prediction along with the $x,y$ features. This is known as contextual information. In this work, a deep exploration of these features is carried out by evaluating different types of data, using different fusion strategies. The core architectures of this model are CNN and LSTM architectures. It is concluded that in the prediction task, not only are the features important, but the way they are fused in the developed architecture is also of importance. Full article

Climate parameter projections obtained by global and regional models (GCM and RCM, respectively) offer a challenge to many researchers in terms of controlling the quality of the outcome data using several scales. In the literature, the proposed models, namely statistical downscaled and regression-based models, are mostly used to adjust the RCM data series. Contrariwise, in practice, these conceptual models perform poorly in certain cases and at certain scales. In this regard, a new downscaling model is proposed herein for annual rainfall projection, based on fusion models, namely polynomial regression (Poly_R), classification and regression tree (CRT), and principal component regression (PCR). The proposed model downscales the rainfall data projected by the coupled model intercomparison phase five (CMIP5) under different representative concentration pathway (RCP) scenarios (2.6, 4.5, 6.0, and 8.5) using overlapping data between the observation and the CMIP5 historical data. This process aims to define the framework for how to use the output equations and algorithm to correct data forecasting by RCM. Generally, the model can be summarized into three levels of analysis, starting with an iterative downscaling using a trendline model that is obtained by Poly_R fitting. Then, the CRT is used to classify and predict the data in subsets. Finally, multiple regression is given by a PCR model using principal components and standardized variables. The final model is also used to downscale the predicted data obtained by both previous models. The results provide the best performance of the fusion model in all RCP cases, compared to the delta change correction and linear scale models. This performance is proved by R² scores which range between 0.87 and 0.95. Full article

Systems of difference equations frequently present dynamically unstable solutions in the long term, which could imply the appearance of complications in the application of vector autoregressive (VAR) models in the Johansen sense, regardless of the precision required. In this work the necessary conditions are presented to guarantee the dynamical convergence of the solutions from the approach of the systems in discrete time series with the stochastic processes. The main aim is to show the importance of dynamic stability in structural-type models with respect to estimator bias. Full article

The accurate prediction of road user behaviour is of paramount importance for the design and implementation of effective trajectory prediction systems. Advances in this domain have recently been centred on incorporating the social interactions between agents in a scene through the use of RNNs. Transformers have become a very useful alternative to solve this problem by making use of positional information in a straightforward fashion. The proposed model leverages positional information together with underlying information of the scenario through goals in the digital map, in addition to the velocity and heading of the agent, to predict vehicle trajectories in a prediction horizon of up to 5 s. This approach allows the model to generate multimodal trajectories, considering different possible actions for each agent, being tested on a variety of urban scenarios, including intersections, and roundabouts, achieving state-of-the-art performance in terms of generalization capability, providing an alternative to more complex models. Full article

Transfer learning has not been widely explored with time series. However, it could boost the application and performance of deep learning models for predicting macroeconomic time series with few observations, like monthly variables. In this study, we propose to generate a forecast of five macroeconomic variables using deep learning and transfer learning. The models were evaluated with cross-validation on a rolling basis and the metric MAPE. According to the results, deep learning models with transfer learning tend to perform better than deep learning models without transfer learning and other machine learning models. The difference between statistical models and transfer learning models tends to be small. Although, in some series, the statistical models had a slight advantage in terms of the performance metric, the results are promising for the application of transfer learning to macroeconomic time series. Full article

This paper introduces a software component created in Visual Basic for Applications (VBA) that can be applied for creating an optimal portfolio using two different methods. The first method is the seminal approach of Markowitz and is based on finding budget shares via the minimization of the variance of the underlying portfolio. The second method, developed by Hatemi-J and El-Khatib, combines risk and return directly in the optimization problem and yields budget shares that lead to maximizing the risk-adjusted return of the portfolio. This approach is consistent with the expectation of rational investors since these investors consider both risk and return as the fundamental basis for the selection of the investment assets. Our package offers another advantage that is usually neglected in the literature, which is the number of assets that should be included in the portfolio. The common practice is to assume that the number of assets is given exogenously when the portfolio is constructed. However, the current software component constructs all possible combinations and thus the investor can figure out empirically which portfolio is the best one among all portfolios considered. The software is consumer-friendly via a graphical user interface. An application is also provided to demonstrate how the software can be used using real-time series data for several assets. Full article

In this work a novel application for multivariable forecasting is presented, applied to hydrological variables and based on a multivariable NARX model. The proposed approach is designed for two hydrological stations located at the Atrato River in Colombia where the variables of water level, water flow and water precipitation are correlated by using the NARX model based on a neural network structure. The structure of the NARX-based neural network is designed in order to consider the complex dynamics of hydrological variables and their corresponding cross-correlations. A short-term water level forecasting is designed based on the NARX model, to be used as an early warning flood system. The validation of the proposed approach is performed by comparing the estimation error with an ARX dynamic model. As a result, it is shown that a NARX model structure is more suitable for water level forecasting than simplified structures. Full article

The easiest approach to customer activity forecasting involves using the whole available and applicable population of customers that a certain data set contains. The drawback of this simple technique is twofold: the set could be too big, and it could contain customers of very different peculiarities, which means that customers whose previous behavior is helpful for the forecast and whose one is not are mixed, and while the first performs a good-quality prediction, the second spoils it by adding noise. Hence, if we could choose the customers with good predictability and put aside the others “as a shepherd divideth his sheep from the goats” (Matthew 25:32), we would solve both problems: less data volume and less noise; the principle is like ancient “divide et impera”. In our research, we developed the method of customers separation by predictability and its dynamics with the help of LSTM models. Our research shows that (1) customer separation helps to improve the forecasting quality of the whole population due to the decomposition of all clients’ time series, and (2) environmental instability such as pandemics or military action can be leveled out with incremental models. Full article

Empirical findings based on a bivariate logistic regression model with two SME categories (successful and failed) indicate that by adding non-financial indicators to the model based on financial variables, the accuracy of forecasting increases significantly. Namely, the total classification error decreases by an average of 26.99%, while the AUROC value increases by an average of 7.33%. In the additional model, with three firm categories (successful, sensitive, and failed), the findings reveal that one financial variable (self-financing) and three non-financial variables (orderly settlement of obligations, export, and age) significantly explain the occurrence of the early stage of SME failure. Full article

The purpose of this research was to determine how we can optimize both customer and seller experiences in a super shop using hyperautomation technology. Here, a smart bot was employed to speed up responses of simple consumer queries by utilizing natural language processing in real time. We also used machine learning frameworks, such as XGBoost, linear regression, random forest, and hybrid models together, to predict future product demand. In addition, data mining methods, such as the Apriori algorithm, FP growth algorithm, and GSP algorithm, were used to find out which algorithm can be used to determine the right way to place a product to increase the super shop sale. Full article

The propensity of data to cluster at extreme values is important for risk assessment. For example, heavy rain over time leads to catastrophic floods. The extremal index is a measure of Extreme Values Theory that allows measurement of the degree of high-value clustering in a time series. Inference about the extremal index requires a prior choice of values for tuning parameters, which impacts the efficiency of existing estimators. In this work, we propose an algorithm that avoids these constraints. Performance is evaluated based on simulations. We also illustrate with real data. Full article

We describe an optimal linear time complexity method for extracting patterns from sliding windows of multivariate time series that depends only on the length of the time series. The method is implemented as an open-source Java library and is used to detect anomalies in multivariate time series. Full article

In this article, we compare two ways of modelling measures of fetal growth. The goal is to impute the missing information for certain ultrasound measurements that are observed at different times and with different numbers of observations. To analyze the effect that other variables have, such as environmental exposure to certain substances or diet, on fetal growth based on these data, we need to handle the information measured at the same instant of time for all the individuals under study, preferably in three time windows of pregnancy (first trimester, week 12; second trimester, week 20; third trimester, week 34). For this, data at these chosen times, in case they are not available, must be imputed from the available information using an appropriate statistical model. One option is to use a linear model, specifically a generalized least squares model that is fitted to the features shown in the data. The other option is to use diffusion processes, estimating their parameters based on the available information. In both options, missing data can be estimated with the unconditional fitted model, conditional on the previous available measurement, or conditional to the closest measurement. Full article

Slope Entropy (SlpEn) is a recently proposed time series entropy estimation method for classification. This method has yielded better results than other similar methods in all the published studies so far. It is based on a signal-gradient thresholding scheme using two parameters, $\delta$ and $\gamma$, in addition to the usual embedded dimension parameter m. In this work, we investigated the possibility of adding one thresholding parameter more, termed $\theta$, and we compared the original method to the new one. The experiment results showed a small improvement using the new method in terms of classification accuracy. However, the temporal cost increased significantly and therefore we concluded it is not worth the extra effort unless maximum accuracy is of utmost importance. Full article

In this paper, the Recurrent Singular Spectrum Decomposition (R-SSD) algorithm is proposed as an improvement over the Recurrent Singular Spectrum Analysis (R-SSA) algorithm for forecasting non-linear and non-stationary narrowband time series. R-SSD modifies the embedding step of the basic SSA method to reduce energy residuals. This paper conducts simulations and real-case studies to investigate the properties of the R-SSD method and compare its performance with R-SSA. The results show that R-SSD yields more accurate forecasts in terms of ratio root mean squared errors (RRMSEs) and ratio mean absolute errors (RMAEs) criteria. Additionally, the Kolmogorov–Smirnov Predictive Accuracy (KSPA) test indicates significant accuracy gains with R-SSD over R-SSA, as it measures the maximum distance between the empirical cumulative distribution functions of recurrent prediction errors and determines whether a lower error leads to stochastically less error. Finally, the non-parametric Wilcoxon test confirms that R-SSD outperforms R-SSA in filtering and forecasting new data points. Full article

Biogas is an important driver in carbon-neutral energy sources. Many biogas digester setups, however, are not well optimized and waste energy or fail to maximize their gas output potential. To optimize these systems, a framework was developed to measure and predict digester systems’ efficiencies by closely monitoring fluid movements. This framework includes a numerical calculation of fluid behavior (Computational Fluid Dynamics (CFD)), and Deep Learning to estimate the fluid shear-rates introduced by the agitator’s action. Additionally, a novel measurement system is presented that can measure the same metrics, as simulated, in real-world environments. Lastly, an outlook is given that presents the options and extensions of the presented setup to reduce prediction error, minimize measuring efforts further, and recommend optimization approaches to the operator. Full article

To better assess the financial contagion through the VaR, several recent studies used copula models. In the same context, this paper addresses the inefficiency of the classical approach such as a normal distribution in modeling the tail risk, by using the conditional Extreme Value Theory (GARCH-EVT), in order to assess extreme risks with contagion effect. The GARCH-EVT approach is a two-stage hybrid method that combines a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) filter with the Extreme Value Theory (EVT). To implement our approach, we use macroeconomic time series from Morocco, Spain, France, and the USA. Full article

This study compares three methods for optimizing the hyper-parameters m (embedding dimension) and $\tau$ (time delay) from Taken’s Theorem for time-series forecasting to train a Support Vector Regression system (SVR). Firstly, we use a method which utilizes Mutual Information for optimizing $\tau$ and a technique referred to as “Dimension Congruence” to optimize m. Secondly, we employ a grid search and random search, combined with a cross-validation scheme, to optimize m and $\tau$ hyper-parameters. Lastly, various real-world time series are used to analyze the three proposed strategies. Full article

At the starting point of this case study, a garrison hospital performed an assessment of the need for treatment when the number of conscripts queuing at reception is at its highest level. The research aims to find out the reasons for conscripts’ perceived long waiting times, which causes absence from the conscripts’ training. According to the predictions made by the queuing simulation, the hospital’s staff are able to receive patients arriving at reception in the morning without the queue time causing undue harm to training. However, during large congestion peaks, the waiting times may become unreasonable, which would require an increase in human resources. Peaks of congestion usually occur at the beginning of the week, as well as on days with heavy military training. Full article

The forecast of the generation of electrical energy from the solar resource is associated with its uncertainty due to the meteorological variations that it presents. Solar power generation forecasts are important for the efficient operation of solar plants. This article shows a methodology entailing a multilayer neural network with backpropagation and input data from a model with time lag coordinates for a horizon of 24 h and beyond. The neural network model was compared with statistical and prediction models numerical time, resulting in a MAPE of 0.57% and a MAE of 69.29 W. Full article

The aeronautical sector is a vital part of the Brazilian industrial landscape, contributing to the development of new technologies and production techniques with potential applications in other industries. However, due to its restricted nature, there are limited studies on implementing improvements in its systems, highlighting the need for attention in specific subareas of companies in this sector. One such area is the production planning department, especially the forecasting techniques applied in the supply chain, which play a crucial role in the operations of any company and are a determining factor in decision making. The objective of this research is to compare the effectiveness of various time-series forecasting methods, including classical statistical methods and neural networks. The study employs a real-time series that depicts the consumption of a specific material extensively used in the production line of a major Brazilian aircraft manufacturer. The proposed forecasting methods are applied, and the results are compared using three different evaluation metrics. The objective is to emphasize the significance of optimizing strategic planning within the industry and the potential savings that can be achieved by selecting the best forecast. In conclusion, the findings of this study can be used to enhance the efficiency of the supply chain and operations of companies in the aeronautical sector. Full article

This paper analyzes how a robust and dynamic forecasting system was designed and implemented to predict material volumes for the inbound logistics network of an international automotive company. The system aims to reduce transportation logistics costs and improve demand capacity planning for freight forwarders. The forecasting horizon is set for 4 months and 12 months ahead in the future. To solve this problem, a time series modeling approach was carried out by using different time series forecasting methods like ARIMA, Neural Networks, Exponential Smoothing, Prophet, Automated Simple Moving Average, Multivariate Time Series, and Ensemble Forecast. Additionally, important data preprocessing methods and a robust model selection framework were used to train the models and select the best-performing one. This is known as Forward Chaining Nested Cross Validation with origin recalibration. The system performance was assessed using the Symmetric Mean Absolute Error (SMAPE). The final version of the forecasting system can deliver 4-month-ahead forecasts with a SMAPE lower than 10% for 86% of all material flow connections. The system’s forecast output is updated on a monthly basis and was integrated into the inbound logistics network system of the company. Full article

This paper illustrates the application of the indirect seasonal adjustment approach to the index series of hours worked per capita from the Istat VELA survey, which is currently seasonally adjusted using the direct approach instead. The experience already gained during the Istat LFS allowed us to test the reliability of the indirect approach on the VELA series. In this case, the use of the indirect approach was twofold: firstly, the seasonally adjusted index series was obtained by seasonally adjusting the series of hours and the series of the employed separately and then relating them. Secondly, for the numerator, as well as for the denominator, the different series disaggregated by the variables of interest were seasonally adjusted separately. Full article

Industry 4.0 has emerged together with relevant technological tools that have enabled the rise of this new industrial paradigm. One of the main employed tools is Machine Learning techniques, which allow us to extract knowledge from raw data and, therefore, devise intelligent strategies or systems to improve actual industrial processes. In this regard, this paper focuses on the development of a prediction system based on Random Forest (RF) to estimate Pentane concentration in advance. The proposed system is validated offline with more than a year of data and is also tested online in an Energy plant of the Basque Country. Validation results show acceptable outcomes for supporting the operator’s decision-making with a tool that infers Pentane concentration in Butane 400 min in advance and, therefore, the quality of the obtained product. Full article

This paper presents a neural network model to estimate arterial blood pressure (ABP) waveforms using electrocardiogram (ECG) and photoplethysmography (PPG) signals and its first two order mathematical derivatives (PPG${}^{\prime }$, PPG${}^{″}$). In order to achieve this objective, a lightweight and optimized neural network architecture has been proposed, made of Conv1D and BiLSTM layers. To train the network, the UCI Database “Cuff-Less Blood Pressure Estimation Data Set” has been used, which contains ECG and PPG signals together with the corresponding ABP waveform data; then the first two PPG derivatives have been computed. Four different configurations and parameter sets have been tested to choose the best structure and set of parameters. Additionally, various batch sizes, numbers of BiLSTM layers, and the presence of a maximum pooling layer have been tested. The best performing model achieves a mean absolute error of around 2.97, which is comparable to the state-of-the-art methods. Results prove deep learning techniques can be effectively used for non-invasive cuffless arterial blood pressure estimation. The lightweight and optimized model can be effectively used for continuous monitoring of blood pressure, which has significant clinical implications. Further research can focus on integrating the proposed model with wearable devices for real-time blood pressure monitoring in daily life. Full article

There have been numerous advances in financial time series forecasting in recent years. Most of them use deep learning techniques. We identified 15 outstanding papers that have been published in the last seven years and have tried to prove the superiority of their approach to forecasting one-dimensional financial time series using deep learning techniques. In order to objectively compare these approaches, we analysed the proposed statistical models and then reviewed and reproduced them. The models were trained to predict, one day in advance, the value of 29 indices and the stock and commodity prices over five different time periods (from 2007 to 2022), with 4 in-sample years and 1 out-of-sample year. Our findings indicated that, first of all, most of these approaches do not beat the naive approach, and only some barely beat it. Most of the researchers did not provide enough data necessary to fully replicate the approach, not to mention the codes. We provide a set of practical recommendations of when to use which models based on the data sample that we provide. Full article

Urban heat islands, characterized by higher temperatures in cities compared to surrounding areas, have been studied using various techniques. However, during heat waves, existing models often underestimate the intensity of these heat islands compared to empirical measurements. To address this, an hourly time-series-based model for predicting heat island intensity during heat wave conditions is proposed. The model was developed and validated using empirical data from the National Monitoring Network in Temuco, Chile. Results indicate a strong correlation (r > 0.98) between the model’s predictions and actual monitoring data. Additionally, the study emphasizes the importance of considering the unique microclimatic characteristics and built environment of each city when modelling urban heat islands. Factors such as urban morphology, land cover, and anthropogenic heat emissions interact in complex ways, necessitating tailored modelling approaches for the accurate representation of heat island phenomena. Full article

The prediction of currency prices is important for investors with foreign currency assets, both for speculation and for hedging the exchange rate risk. Classical time series models such as ARIMA models were relevant until the advent of neural networks. In particular, recurrent neural networks such as long short-term memory (LSTM) are show to be a good alternative model for the prediction of short-term stock prices. In this paper, we present a comparison between the ARIMA model and LSTM neural network. A hybrid model that combines the two models is also presented. In addition, the effectiveness of this model on Bitcoin’s future contract is analysed. Full article

The problem of anomaly detection in time series has recently received much attention, but in most practical applications, labels for normal and anomalous data are not available. Furthermore, reasons for anomalous results must often be determined. In this paper, we propose a new anomaly detection method based on the expectation–maximization algorithm, which learns the probabilistic behavior of local patterns inherent in time series in an unsupervised manner. The proposed method is simple yet enables anomaly detection with accuracy comparable with that of the conventional method. In addition, the representation of local patterns based on probabilistic models provides new insight that can be used to determine reasons for anomaly detection decisions. Full article

Water resource forecasting plays a crucial role in managing hydrological reservoirs, supporting operational decisions ranging from the economy to energy. In recent years, machine learning-based models, including sequential models such as Long Short-Term Memory (LSTM) networks, have been widely employed to address this task. Despite the significant interest in forecasting hydrological series, weather’s nonlinear and stochastic nature hampers the development of accurate prediction models. This work proposes a Variational Gaussian Process-based forecasting methodology for multiple outputs, termed MOVGP, that provides a probabilistic framework to capture the prediction uncertainty. The case study focuses on the Useful Volume and the Streamflow Contributions from 23 reservoirs in Colombia. The results demonstrate that MOVGP models outperform classical LSTM and linear models in predicting several horizons, with the added advantage of offering a predictive distribution. Full article

GNSS systems allow precise resolution of the geodetic positioning problem through advanced techniques of GNSS observation processing (PPP or relative positioning). Current instrumentation and communications capabilities allow obtaining geocentric and topocentric geodetic high frequency time series, whose analysis provides knowledge of the tectonic or volcanic geodynamic activity of a region. In this work, a GNSS time series study is carried out through the use and adaptation of R packets to determine their behavior, obtaining displacement velocities, noise levels, precursors in the time series, anomalous episodes and their temporal forecast. Statistical and analytical methods are studied; for example, ARMA, ARIMA models, least-squares methods, wavelet functions, Kalman techniques and CATS analysis. To obtain a geodynamic model of the Central American region, the horizontal and vertical velocities obtained by applying the above methods are taken, choosing the velocity with the least margin of error. Significant GNSS time series are obtained in geodynamically active regions (tectonic and/or volcanic). Full article

The purpose of this study is the quantitative investigation of the basketball tournament of the FIBA World Cup 2019. Firstly, it identified the performance of a team by using Principal Components Analysis (PCA). Then, the contributions of shooting, rebounding, turnover, and free-throw factors are identified and compared with Offense vs. Defense in terms of their contribution to the team’s performance. Moreover, other factors are identified that affected the performance, the teams which performed better than expected are detected, and finally, machine learning models which enhance the ‘Power Rankings’ for the prediction of the final position of the teams in the tournament are suggested. Full article

The globalization process and the war in Ukraine show us that migration is one of the strongest global trends in the modern economy. For this paper, we determined three types of migration, depending on the intention of the people involved, these being labor, educational, and refugee migration. Each type has a different influence on the macroeconomic process. However, in this paper, we investigate the influence of general migration on GDP. We analyze five factors that have major influences on GDP, namely, migration (I), interest rate (IR), active population (AP), export (E), and the consumer price index (CPI). For the purposes of this paper, vector autoregressive models (VAR models) were chosen to perform the analysis. We used the Granger causality test to investigate the lag structure and identified the exogenous variables in the VAR model, such as GDP, migration, and the active population. We investigated the cross-influence between these factors and found that migration has a negative effect on the active population and a positive effect on GDP, while GDP growth leads to a decrease in migration. The Akaike and Schwartz criteria showed the high quality of the VAR models. The impulse analysis of shock influences identifies the structure of the reaction seen in GDP and migration, depending on their shock factors. Using decomposition analysis, we found that migration and GDP influence each other by 10–14%, which can improve the forecasting of these factors and the study of structural migration by the use of these three types. Full article

We wanted to determine whether we could use an automated machine learning system called Azure for the selection process and placement of conscript training in such a way that AI can make decisions for the right conscript training program individually. To test this, we had four separate datasets and access to the Microsoft Azure automated machine learning environment. According to the test sets we performed, we see that, by using an automated machine learning environment, it was possible to reach the precision level of the decisions we wanted. The main obstacle was not the used automated machine learning environment itself, but the quality of the data used for learning. We also made improvement suggestions regarding how data could be collected and what kind of data we should measure to make predictive data analysis better and be more usable in the future. Full article

The use of AI and digitalization in many areas of everyday life holds great potential but also introduces significant societal transitions. This paper takes a closer look at three exemplary areas of central social and psychological relevance that might serve as a basis for forecasting transitions in the digital society: (1) social norms in the context of digital systems; (2) surveillance and social scoring; and (3) artificial intelligence as a decision-making aid or decision-making authority. For each of these areas, we highlight current trends and developments and then present future scenarios that illustrate possible societal transitions, related questions to be answered, and how such predictions might inform responsible technology design. Full article

In order to achieve a more efficient allocation of healthcare resources in the near future, it is crucial to understand the patterns and causes of excess mortality and hospitalizations. Neonatal mortality still poses a significant challenge, particularly in developed nations where the mortality rates are already low and healthcare resources are generally available to most of the population. Furthermore, the low mortality rates mean that the data available for modeling are often very limited, restricting the modeling methods that can be used. It is also important that the chosen methods allow for explainable, non-black-box models that can be interpreted by healthcare professionals. Considering these challenges, the work hereby presented thoroughly analyzed the time series of the neonatal mortality rates in Portugal between 2014 and 2019 in terms of trend and seasonal patterns. The applicability and performance of different data-based methods were also analyzed. Furthermore, the mortality rates were studied in terms of their relation to environmental variables, such as temperature and air pollution indicators, with the goal of establishing causal relations between such variables and excess mortality. The preliminary results show that ARMA, neural and fuzzzy models are able to forecast the studied mortality rates with good performance. In particular, neural models have the best predictive performance, while fuzzy models are well suited to obtain interpretable models with acceptable performance. Full article

In time series analyses, the auto-regressive (AR) modelling of zero mean data is widely used for system identification, signal decorrelation, detection of outliers and forecasting. An AR process of order p is uniquely defined by p coefficients and the variance in the noise. The roots of the characteristic polynomial can be used as an alternative parametrization of the coefficients, which can be used to construct a continuous covariance function of the AR process or to verify that the AR process is stationary. In a previous study, we introduced an AR process of time variable coefficients (TVAR process) in which the movement of the roots was specified as a polynomial of order one. Until now, this method was analytically derived only for TVAR processes of orders one and two. Thus, higher-level processes had to be assembled by the successive estimation of these process orders. In this contribution, the analytical solution for a TVAR(3) process is derived and compared to the successive estimation using a TVAR(1) and TVAR(2) process. We will apply the proposed approach to a GNSS time series and compare the best-fit TVAR(3) process with the best-fit composition of TVAR(2) and TVAR(1) process. Full article

Environmental demands around the world have led to an increasing interest in natural gas due to its advantages over other hydrocarbons used in power generation, which has led to the search for the best way to solve the transportation problem associated with this resource. In this paper, we propose a methodology that allows us to address the non-convexity related to the Weymouth equation that makes the optimization problem so difficult. The mentioned equation, in charge of relating the flows through the pipelines and the pressures at the nodes, is characterized by having a discontinuity in the form of a sign function. The proposal of this work is based on the use of Mathematical Programs with Complementarity Constraints (MPCC) to achieve a good approximation since it allows make certain continuous variables to behave as discrete variables in such a way that it is possible to avoid having to pose a mixed integer programming problem and this one. This approach showed a smaller approximation error (or at least equal) with other approximations used in the state of the art when tested in three different networks: one of 8 nodes, one of 48 nodes tested in other related works, and one of 63 nodes representing the Colombian natural gas transportation system. Full article

This study suggests using wearable activity trackers to identify mobility patterns in chronic complex patients (CCPs) and investigate their relation with the Barthel index (BI) to assess functional decline. CCPs are individuals who suffer from multiple, chronic health conditions that often lead to a progressive decline in their functional capacity. As a result, CCPs frequently require the use of healthcare and social resources, placing a significant burden on the healthcare system. Evaluating mobility patterns is critical for determining a CCP’s functional capacity and prognosis. To monitor the overall activity levels of CCPs, wearable activity trackers have been proposed. Utilizing the data gathered by the wearables, time series clustering with dynamic time warping (DTW) is employed to generate synchronized mobility patterns of the mean activity and coefficient of variation profiles. The research has revealed distinct patterns in individuals’ walking habits, including the time of day they walk, whether they walk continuously or intermittently, and their relation to BI. These findings could significantly enhance CCPs’ quality of care by providing a valuable tool for personalizing treatment and care plans. Full article

We present a sparsity-promoting RBF algorithm for time-series prediction. We use a time-delayed embedding framework and model the function from the embedding space to predict the next point in the time series. We explore the standard benchmark data set generated by the Mackey–Glass chaotic dynamical system. We also consider a model of temperature telemetry associated with the mouse model immune response to infection. We see that significantly reduced models can be obtained by solving the penalized RBF fitting problem. Full article

Flood modelling is essential for addressing a range of scientific and engineering challenges. In recent years, the high computational demands of solving shallow water equations numerically have led researchers to explore machine-learning-based emulators for predicting floods and flood risk. Specifically, the proliferation of convolutional neural networks in solving different scientific problems has encouraged researchers to investigate their applicability in flood modelling. Most of these studies, however, have focused on specific locations or hydrological conditions, meaning that their findings may not be directly applicable to other situations without additional data and further training. We present here a U-Net model, a popular deep learning algorithm, which has the capacity to approximate maximum flood depths across multiple return periods while maintaining catchment generalizability. The model was trained using the outputs from a 2D hydraulic model (JFlow) to predict maximum water depths for a set of rainfall return periods (20, 100 and 1000 years). The pre-trained model was then applied to estimate depths in three unseen catchment areas. Our results demonstrate that U-Net can be used to approximate water depths in previously unseen catchments with significantly less computational time compared to the 2D model. Full article

Macroeconomic adverse selection is computed as a time series of forecast residuals via the vintage origination model for an industry dataset of auto loans. The adverse selection time series are computed separately as model residuals using logistic regression, neural networks, and stochastic gradient boosted trees to predict defaults in the first 24 months of a loan. Panel data versions of these models with lifecycle and environment inputs from a segmented Age-Period-Cohort analysis were also estimated. The estimates show that panel data methods make better use of available data to provide faster estimates of adverse selection risk in recent vintages and incorporate defaults at any age of the loan. The nonlinear modeling advantages of neural networks and stochastic gradient boosted trees did not significantly alter the estimates of adverse selection. Overall, all methods confirmed that macroeconomic adverse selection was dramatically higher in 2021 and 2022 for US auto loan originations. Full article

We apply a Granger causality and auto-correlation analysis to train a recurrent neural network (RNN) that acts as a virtual sensor model. These models can be used to check the status of several hundreds of sensors during turbo-machinery units’ operation. Checking the health of each sensor is a time-consuming activity. Training a supervised algorithm is not feasible because we do not know all the failure modes that the sensors can undergo. We use a semi-supervised approach and train an RNN (LSTM) on non-anomalous data to build a virtual sensor using other sensors as regressors. We use the Granger causality test to identify the set of input sensors for a given target sensor. Moreover, we look at the auto-correlation function (ACF) to understand the temporal dependency in data. We then compare the predicted signal vs. the real one to raise (in case) an anomaly in real time. Results report 96% precision and 100% recall. Full article

As a small and open economy, Uruguay is highly exposed to international and regional shocks that affect domestic uncertainty. To account for this uncertainty, we construct two geometric uncertainty indices (based on the survey of industrial expectations about the economy and the export market) and explore their association with the Uruguayan GDP cycle between 1998 and 2022. Based on the estimated linear ARDL models that showed negative but weak relationships between the uncertainty indices and the GDP cycle, we test for the existence of structural breaks in these relationships. Although we find a significant break in 2003 for both indices and another in 2019 for one of them, Wald tests performed on the non-linear models only confirm the structural break in the early 2000s in the model with the index based on export market expectations. In this non-linear model, we find that the negative influence of uncertainty fades after 2003. The evidence of a differential influence before and after this date remains, even when controlling for the variability in non-tradable domestic prices. Two implications arise from these results. First, the evidence of relevant changes that made the Uruguayan economy less vulnerable from 2003 onward. Second, the importance of the expectation about the future of the export market in the macroeconomic cycle of a small and open economy like Uruguay. Full article

A computationally efficient predictive digital twin (DT) of a small-scale greenhouse needs an accurate and faster modelling of key variables such as the temperature field and flow field within the greenhouse. This involves: (a) optimally placing sensors in the experimental set-up and (b) developing fast predictive models. In this work, for a greenhouse set-up, the former requirement fulfilled first by identifying the optimal sensor locations for temperature measurements using the QR column pivoting on a tailored basis. Here, the tailored basis is the low-dimensional representation of hi-fidelity computational fluid dynamics (CFD) flow data, and these tailored basis are obtained using proper orthogonal decomposition (POD). To validate the method, the full temperature field inside the greenhouse is then reconstructed for an unseen parameter (inflow condition) using the temperature values from a few synthetic sensor locations in the CFD model. To reconstruct the flow-fields using a faster predictive model than the hi-fidelity CFD model, a long-short term memory (LSTM) method based on a reduced-order model (ROM) is used. The LSTM learns the temporal dynamics of coefficients associated with the POD-generated velocity basis modes. The LSTM-POD ROM model is used to predict the temporal evolution of velocity fields for our DT case, and the predictions are qualitatively similar to those obtained from hi-fidelity numerical models. Thus, the two data-driven tools have shown potential in enabling the forecasting and monitoring of key variables in a digital twin of a greenhouse. In future work, there is scope for improvements in the reconstruction accuracy by involving deep-learning-based corrective source term approaches. Full article

In this article, we introduce an AI-enhanced study planning solution named Careerbot, which is a service designed to help students with their “forecasted self”. We define a new term “forecasted self” to mean a future-oriented digital twin, where a student can explore several future selves equipped with new, acquired skills for projected future jobs. The future jobs domain here includes knowledge-work related jobs related to digitalization, emerging technologies, and Industry 4.0/Society 5.0. Full article

This research examines how much forecasting accuracy can be achieved by modelling the relationships between listed real estate and macroeconomic time series variables using the logit regression model. The example data for this analysis included 10-year (2008–2018) transactions. The Statistical Package for Social Sciences (SPSS, version 25) and Microsoft Excel 2016 were used for descriptive and inferential analysis. The data collected on the listed real estate transactions for South Africa and Nigeria represent the largest listed real estate markets in the continent. The study found that 22.2% variance in the Nigerian real estate market was explained by the lending rate, treasure bill rate, and Consumer Price Index, while 9.4% variance in the South African real estate market was explained by changes in the exchange rate and coincident indicators. The strength and similarity of the model capacity in both countries showed that each market signal has a predictive accuracy of 75% (Nigeria) and 80% (South Africa). Full article

Unexpected responses in dynamic systems can lead to catastrophic failures. Without full knowledge of the system, it is impossible to know whether all of the dynamics have been captured or considered. Furthermore, a large number of Monte Carlo simulations may be time-prohibitive when looking at extreme behavior. In this paper, the Matched Upcrossing Equivalent Linear System (MUELS) linearization method is applied to a series of Duffing oscillators of varying stationarities, characterized by brief excursions into domains of much larger oscillation, to test the non-linear limits of the MUELS method and the ability of the MUELS method to uncover rare dynamics. The MUELS method is a linearization scheme that searches for linear systems that have the same zero-upcrossing rate as the non-linear system of interest. These systems are then input into the Design Loads Generator (DLG) to produce an ensemble of input time series that lead to extreme linear realizations, which are then used as input into the non-linear system of interest. The MUELS method results were compared to Monte Carlo simulations in various ways including probability density functions, time series, and computational expense. It was found that the MUELS method recovers extreme behavior with relative success, seeing more accurate results for more stationary systems. The current work suggests that improvements to return period estimation and equivalent linear system parameter fidelity could produce even more accurate results. Full article

The tectonic activity produced by the interaction between the Eurasian and African plates continually generates high seismic activity and the possibility of tsunamis occurring in the Gulf of Cadiz, Spain. The occurrence of these phenomena and the associated threat implies the need to implement a seismogeodesic system made up of a GNSS receiver, a seismograph–accelerograph, and an inclinometer that allows for us to study the behavior of tectonic activity in the Gulf and adjacent areas. This system is installed in the Doñana biological station, Huelva, Spain, and sends continuous records to the control center located in the University of Cadiz, generating GNSS, seismic, accelerometric, and inclinometric time series, which, together with the implementation of geodetic and geophysical techniques, is capable of providing information on tectonic activity immediately. In this manuscript, the time series generated by the system have been analyzed, in addition to a specific seismic event that occurred in the study area. Full article

The signal coming from the artificial satellites of the GNSS system suffers various effects that considerably decrease the precision in solving the positioning problem. To mathematically model these effects, the atmosphere is divided into two main parts, the troposphere and the ionosphere. The troposphere can only be modelled, while the ionospheric effect can be modeled or eliminated depending on the geodetic sophistication of the receivers used. In this way, information is obtained about both layers of the atmosphere. For tropospheric modeling, the parameters of total zenithal delay (ZTD) or precipitable water vapor (PVW) will be taken, and for the ionosphere the total electron content (TEC) will be taken. In this work, statistical and analytical techniques will be applied with the R software; for example, ARMA, ARIMA models, least squares methods, wavelet functions, Kalman techniques, and CATS analysis. With this, the anomalies that occurred in the values of the ZTD and TEC in the case of the 2021 eruption of the Cumbre Vieja volcano on the island of La Palma. Full article