Engineering Proceedings, Volume 39, 2023

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 simulat...

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 applica...

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-ye...

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...

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 sens...

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 expectati...

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’...

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 emulat...

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 re...

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 s...

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 multip...

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 associat...

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 th...

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, particul...

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 mig...

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, r...

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 bein...

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...

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-T...

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...

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...

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...

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 superi...

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 network...

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...

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, ther...

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 al...

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 cost...

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...

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...

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&rsquo...

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 usin...

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 wh...

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 meas...

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 m...

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...

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 thresholdin...

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...

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...

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 hi...

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 pa...

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 pr...

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 natu...

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 signif...

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...

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 carrie...

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 Col...

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 betwee...

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...

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 ind...