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Forecasting, Volume 3, Issue 1 (March 2021) – 15 articles

Cover Story (view full-size image): Economic forecasting is difficult, largely because of the many sources of non-stationarity affecting observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of different forecasting methods can be evaluated. We propose 8 general principles that seem important for successful forecasting and show their relevance to the methods that did well in the M4 competition. We establish some general properties of the M4 data set of 100,000 time series, and use these together with the principles to improve the benchmark predictors, as well as the Card method we created and used in that competition. A data generation process that captures the salient features of the M4 annual data is proposed. View this paper
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14 pages, 9492 KiB  
Article
Load Forecasting in an Office Building with Different Data Structure and Learning Parameters
by Daniel Ramos, Mahsa Khorram, Pedro Faria and Zita Vale
Forecasting 2021, 3(1), 242-255; https://doi.org/10.3390/forecast3010015 - 20 Mar 2021
Cited by 23 | Viewed by 3528
Abstract
Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the [...] Read more.
Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the electricity consumption for the future to adequately perform the energy management. In the present paper, a load forecasting service designed for office buildings is implemented. In the building, using several available sensors, different learning parameters and structures are tested for artificial neural networks and the K-nearest neighbor algorithm. Deep focus is given to the individual period errors. In the case study, the forecasting of one week of electricity consumption is tested. It has been concluded that it is impossible to identify a single combination of learning parameters as different parts of the day have different consumption patterns. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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14 pages, 5223 KiB  
Article
A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency Regulation Services
by Pavlos Nikolaidis and Harris Partaourides
Forecasting 2021, 3(1), 228-241; https://doi.org/10.3390/forecast3010014 - 17 Mar 2021
Cited by 7 | Viewed by 2859
Abstract
The intermittent and uncontrollable power output from the ever-increasing renewable energy sources, require large amounts of operating reserves to retain the system frequency within its nominal range. Based on day-ahead load forecasts, many research works have proposed conventional and stochastic approaches to define [...] Read more.
The intermittent and uncontrollable power output from the ever-increasing renewable energy sources, require large amounts of operating reserves to retain the system frequency within its nominal range. Based on day-ahead load forecasts, many research works have proposed conventional and stochastic approaches to define their optimum margins for reliability enhancement at reasonable production cost. In this work, we aim at delivering real-time load forecasting to lower the operating-reserve requirements based on intra-hour weather update predictors. Based on critical predictors and their historical data, we train an artificial model that is able to forecast the load ahead with great accuracy. This is a feed-forward neural network with two hidden layers, which performs real-time forecasts with the aid of a predictive model control developed to update the recommendations intra-hourly and, assessing their impact and its significance on the output target, it corrects the imposed deviations. Performing daily simulations for an annual time-horizon, we observe that significant improvements exist in terms of decreased operating reserve requirements to regulate the violated frequency. In fact, these improvements can exceed 80% during specific months of winter when compared with robust formulations in isolated power systems. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
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21 pages, 1874 KiB  
Article
Robust Non-Parametric Mortality and Fertility Modelling and Forecasting: Gaussian Process Regression Approaches
by Ka Kin Lam and Bo Wang
Forecasting 2021, 3(1), 207-227; https://doi.org/10.3390/forecast3010013 - 9 Mar 2021
Cited by 6 | Viewed by 3700
Abstract
A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. An accurate model for forecasting demographic movements is important for decision making in social welfare policies and resource budgeting among the government and [...] Read more.
A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. An accurate model for forecasting demographic movements is important for decision making in social welfare policies and resource budgeting among the government and many industry sectors. This article introduces a novel non-parametric approach using Gaussian process regression with a natural cubic spline mean function and a spectral mixture covariance function for mortality and fertility modelling and forecasting. Unlike most of the existing approaches in demographic modelling literature, which rely on time parameters to determine the movements of the whole mortality or fertility curve shifting from one year to another over time, we consider the mortality and fertility curves from their components of all age-specific mortality and fertility rates and assume each of them following a Gaussian process over time to fit the whole curves in a discrete but intensive style. The proposed Gaussian process regression approach shows significant improvements in terms of forecast accuracy and robustness compared to other mainstream demographic modelling approaches in the short-, mid- and long-term forecasting using the mortality and fertility data of several developed countries in the numerical examples. Full article
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26 pages, 2817 KiB  
Review
Trends in Using IoT with Machine Learning in Health Prediction System
by Amani Aldahiri, Bashair Alrashed and Walayat Hussain
Forecasting 2021, 3(1), 181-206; https://doi.org/10.3390/forecast3010012 - 7 Mar 2021
Cited by 110 | Viewed by 14478
Abstract
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT [...] Read more.
Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends. Full article
(This article belongs to the Special Issue Forecasting with Machine Learning Techniques)
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15 pages, 1547 KiB  
Article
Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes
by Alexander Faehnle and Mariangela Guidolin
Forecasting 2021, 3(1), 166-180; https://doi.org/10.3390/forecast3010011 - 5 Mar 2021
Cited by 8 | Viewed by 4020
Abstract
In an environment such as e-commerce, characterized by the presence of numerous agents, competition based on product characteristics is a very important aspect. This paper proposes a model based on vector autoregressive processes (VAR) and Lasso penalization to detect and examine the dynamics [...] Read more.
In an environment such as e-commerce, characterized by the presence of numerous agents, competition based on product characteristics is a very important aspect. This paper proposes a model based on vector autoregressive processes (VAR) and Lasso penalization to detect and examine the dynamics that govern real-time price competition in electronic marketplaces. Employing this model, an empirical study was performed on the price trends of smartphone models on the major electronic sales platforms of the Italian market. The proposed model detects real-time price variations in single vendors, based on the variations of their direct competitors. The statistical method adopted in this analysis may be useful for e-commerce companies that conduct market analyses of competitors’ pricing strategies. Full article
(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)
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28 pages, 671 KiB  
Article
Forecasting Principles from Experience with Forecasting Competitions
by Jennifer L. Castle, Jurgen A. Doornik and David F. Hendry
Forecasting 2021, 3(1), 138-165; https://doi.org/10.3390/forecast3010010 - 23 Feb 2021
Cited by 13 | Viewed by 6071
Abstract
Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We [...] Read more.
Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that did well in the M4 competition. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as the Card method that we created for our submission to that competition. A data generation process is proposed that captures the salient features of the annual data in M4. Full article
(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)
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3 pages, 167 KiB  
Editorial
Editorial for Special Issue: “Feature Papers of Forecasting”
by Sonia Leva
Forecasting 2021, 3(1), 135-137; https://doi.org/10.3390/forecast3010009 - 21 Feb 2021
Cited by 3 | Viewed by 1892
Abstract
Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications [...] Full article
(This article belongs to the Special Issue Feature Papers of Forecasting)
22 pages, 2294 KiB  
Article
Forecasting with Business and Consumer Survey Data
by Oscar Claveria
Forecasting 2021, 3(1), 113-134; https://doi.org/10.3390/forecast3010008 - 17 Feb 2021
Cited by 1 | Viewed by 3999
Abstract
In a context of growing uncertainty caused by the COVID-19 pandemic, the opinion of businesses and consumers about the expected development of the main variables that affect their activity becomes essential for economic forecasting. In this paper, we review the research carried out [...] Read more.
In a context of growing uncertainty caused by the COVID-19 pandemic, the opinion of businesses and consumers about the expected development of the main variables that affect their activity becomes essential for economic forecasting. In this paper, we review the research carried out in this field, placing special emphasis on the recent lines of work focused on the exploitation of the predictive content of economic tendency surveys. The study concludes with an evaluation of the forecasting performance of quarterly unemployment expectations for the euro area, which are obtained by means of machine learning methods. The analysis reveals the potential of new analytical techniques for the analysis of business and consumer surveys for economic forecasting. Full article
(This article belongs to the Section Forecasting in Economics and Management)
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11 pages, 256 KiB  
Article
Construction of a Predictive Model for MLB Matches
by Chia-Hao Chang
Forecasting 2021, 3(1), 102-112; https://doi.org/10.3390/forecast3010007 - 16 Feb 2021
Cited by 2 | Viewed by 4489
Abstract
The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: [...] Read more.
The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: first, they are used to quantify the amount of profit made by the bettors; second, they are regarded as a market equilibrium point between multiple bookmakers and bettors. If the bettors have a more accurate prediction model than the system used to produce betting odds, it will create a positive expected return for the bettors. In this article, we used the Markov process method and the runner advancement model to estimate the expected runs in an MLB match for the teams based on the batting lineup and the pitcher. Full article
(This article belongs to the Section Forecasting in Computer Science)
11 pages, 409 KiB  
Article
Electrical Load Forecast by Means of LSTM: The Impact of Data Quality
by Alfredo Nespoli, Emanuele Ogliari, Silvia Pretto, Michele Gavazzeni, Sonia Vigani and Franco Paccanelli
Forecasting 2021, 3(1), 91-101; https://doi.org/10.3390/forecast3010006 - 8 Feb 2021
Cited by 21 | Viewed by 3353
Abstract
Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in [...] Read more.
Accurate forecast of aggregate end-users electric load profiles is becoming a hot topic in research for those main issues addressed in many fields such as the electricity services market. Hence, load forecast is an extremely important task which should be understood more in depth. In this research paper, the dependency of the day-ahead load forecast accuracy on the basis of the data typology employed in the training of LSTM has been inspected. A real case study of an Italian industrial load with samples recorded every 15 min for the year 2017 and 2018 was studied. The effect in the load forecast accuracy of different dataset cleaning approaches was investigated. In addition, the Generalised Extreme Studentized Deviate hypothesis testing was introduced to identify the outliers present in the dataset. The populations were constructed on the basis of an autocorrelation analysis that allowed for identifying a weekly correlation of the samples. The accuracy of the prediction obtained from different input dataset has been therefore investigated by calculating the most commonly used error metrics, showing the importance of data processing before employing them for load forecast. Full article
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35 pages, 2606 KiB  
Article
Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data
by Monica Defend, Aleksey Min, Lorenzo Portelli, Franz Ramsauer, Francesco Sandrini and Rudi Zagst
Forecasting 2021, 3(1), 56-90; https://doi.org/10.3390/forecast3010005 - 8 Feb 2021
Cited by 3 | Viewed by 2702
Abstract
This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine [...] Read more.
This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns. Full article
(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)
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17 pages, 3820 KiB  
Article
Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)
by Rodgers Makwinja, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Bandulo Phiri, Ishmael Bobby Mphangwe Kosamu and Chikumbusko Chiziwa Kaonga
Forecasting 2021, 3(1), 39-55; https://doi.org/10.3390/forecast3010004 - 8 Feb 2021
Cited by 14 | Viewed by 4004
Abstract
Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, [...] Read more.
Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024. Full article
(This article belongs to the Section Environmental Forecasting)
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2 pages, 177 KiB  
Editorial
Acknowledgment to Reviewers of Forecasting in 2020
by Forecasting Editorial Office
Forecasting 2021, 3(1), 37-38; https://doi.org/10.3390/forecast3010003 - 25 Jan 2021
Viewed by 1844
Abstract
Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Forecasting maintains its standards for the high quality of its published papers [...] Full article
20 pages, 39676 KiB  
Article
Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network
by Matteo Picozzi and Antonio Giovanni Iaccarino
Forecasting 2021, 3(1), 17-36; https://doi.org/10.3390/forecast3010002 - 4 Jan 2021
Cited by 18 | Viewed by 3995
Abstract
Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even [...] Read more.
Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes. Full article
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16 pages, 971 KiB  
Article
Modeling Post-Liberalized European Gas Market Concentration—A Game Theory Perspective
by Hassan Hamie, Anis Hoayek and Hans Auer
Forecasting 2021, 3(1), 1-16; https://doi.org/10.3390/forecast3010001 - 28 Dec 2020
Cited by 3 | Viewed by 3233
Abstract
The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model [...] Read more.
The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model European gas markets and their effect on prices. In this research, the parametric and nonparametric game theory methods are employed to study the effect of the market concentration on gas prices. The parametric method takes into account the classical Cournot equilibrium test, with assumptions on cost and demand functions. However, the non-parametric method does not make any prior assumptions, a factor that allows greater freedom in modeling. The results of the parametric method demonstrate that the gas suppliers’ behavior in Austria and The Netherlands gas markets follows the Nash–Cournot equilibrium, where companies act rationally to maximize their payoffs. The non-parametric approach validates the fact that suppliers in both markets follow the same behavior even though one market is more liquid than the other. Interestingly, our findings also suggest that some of the gas suppliers maximize their ‘utility function’ not by only relying on profit, but also on some type of non-profit objective, and possibly collusive behavior. Full article
(This article belongs to the Special Issue Forecasting Commodity Markets)
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