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8 pages, 455 KiB

Open AccessProceeding Paper

Explaining When Deep Learning Models Are Better for Time Series Forecasting

by Martín Solís and Luis-Alexander Calvo-Valverde

Eng. Proc. 2024, 68(1), 1; https://doi.org/10.3390/engproc2024068001 - 27 Jun 2024

Viewed by 161

There is a gap of knowledge about the conditions that explain why a method has a better forecasting performance than another. Specifically, this research aims to find the factors that can influence deep learning models to work better with time series. We generated [...] Read more.

There is a gap of knowledge about the conditions that explain why a method has a better forecasting performance than another. Specifically, this research aims to find the factors that can influence deep learning models to work better with time series. We generated linear regression models to analyze if 11 time series characteristics influence the performance of deep learning models versus statistical models and other machine learning models. For the analyses, 2000 time series of M4 competition were selected. The results show findings that can help explain better why a pretrained deep learning model is better than another kind of model. Full article

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11 pages, 1006 KiB

Open AccessProceeding Paper

The Cassandra Method: Dystopian Visions as a Basis for Responsible Design

by Sarah Diefenbach and Daniel Ullrich

Eng. Proc. 2024, 68(1), 2; https://doi.org/10.3390/engproc2024068002 - 27 Jun 2024

Viewed by 81

Abstract

Innovative technologies often have unforeseen negative consequences on an individual, societal, or environmental level. To minimize these, the Cassandra method aims to foresee such negative effects by systematically investigating dystopian visions. Starting with the activation of a (self-)critical mindset, the next steps are [...] Read more.

Innovative technologies often have unforeseen negative consequences on an individual, societal, or environmental level. To minimize these, the Cassandra method aims to foresee such negative effects by systematically investigating dystopian visions. Starting with the activation of a (self-)critical mindset, the next steps are collecting a maximum number of negative effects and assessing their relevance. Finally, the envisioned impairments are used to improve the product concepts in a responsible way. This paper broadly outlines the method, its applications during product development and research, and reports on experiences from an expert workshop. Full article

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8 pages, 571 KiB

Open AccessProceeding Paper

Forecasting Methods for Road Accidents in the Case of Bucharest City

by Cristina Oprea, Eugen Rosca, Ionuț Preda, Anamaria Ilie, Mircea Rosca and Florin Rusca

Eng. Proc. 2024, 68(1), 3; https://doi.org/10.3390/engproc2024068003 - 27 Jun 2024

Viewed by 149

Abstract

This paper aims to emphasize the necessity for policy reform, improvements in vehicle design and enhanced public awareness through the projection of future trends in road accidents, injuries and fatalities. The statistical methods that are used in this study are the empirical laws [...] Read more.

This paper aims to emphasize the necessity for policy reform, improvements in vehicle design and enhanced public awareness through the projection of future trends in road accidents, injuries and fatalities. The statistical methods that are used in this study are the empirical laws of Smeed and Andreassen. The main gap that the researchers identify is the lack of a standardized methodology with the help of which the appropriate forecasting method can be chosen in the area of traffic accidents. In the present study, the authors propose such a methodology that can be generalized, being suitable for use for any urban agglomeration at the micro and macro level. Full article

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8 pages, 880 KiB

Open AccessProceeding Paper

Deep Learning for Crime Forecasting of Multiple Regions, Considering Spatial–Temporal Correlations between Regions

by Martín Solís and Luis-Alexander Calvo-Valverde

Eng. Proc. 2024, 68(1), 4; https://doi.org/10.3390/engproc2024068004 - 28 Jun 2024

Viewed by 154

Abstract

Crime forecasting has gained popularity in recent years; however, the majority of studies have been conducted in the United States, which may result in a bias towards areas with a substantial population. In this study, we generated different models capable of forecasting the [...] Read more.

Crime forecasting has gained popularity in recent years; however, the majority of studies have been conducted in the United States, which may result in a bias towards areas with a substantial population. In this study, we generated different models capable of forecasting the number of crimes in 83 regions of Costa Rica. These models include the spatial–temporal correlation between regions. The findings indicate that the architecture based on an LSTM encoder–decoder achieved superior performance. The best model achieved the best performance in regions where crimes occurred more frequently; however, in more secure regions, the performance decayed. Full article

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9 pages, 282 KiB

Open AccessProceeding Paper

Optimizing Social Security Contributions for Spanish Self-Employed Workers: Combining Data Preprocessing and Ensemble Models for Accurate Revenue Estimation

by Luis Palomero, Vicente García and José Salvador Sánchez

Eng. Proc. 2024, 68(1), 5; https://doi.org/10.3390/engproc2024068005 - 28 Jun 2024

Viewed by 152

Abstract

The Real Decreto-ley 13/2022 has amended the framework governing the calculation of Social Security contributions for Spanish self-employed workers. This framework obligates taxpayers to the annual revenue projection, under the possibility of lending money for free or paying unexpected taxes at the end [...] Read more.

The Real Decreto-ley 13/2022 has amended the framework governing the calculation of Social Security contributions for Spanish self-employed workers. This framework obligates taxpayers to the annual revenue projection, under the possibility of lending money for free or paying unexpected taxes at the end of the year in the case of deviations. To address this issue, the Declarando firm has developed an algorithm to recommend the optimal contributions that combines a Simple Moving Average forecasting method with an offset-adjustment technique. This paper examines how this strategy can be improved by cleaning the input data and combining different forecasts using an Ensemble-based approach. After testing experimentally various alternatives, a promising strategy involves employing a median-based Ensemble on preprocessed data. Although this Ensemble-based approach significantly reduces forecasting errors, the improvements are diluted when the predictions are combined with the offset-adjustment process. Full article

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7 pages, 668 KiB

Open AccessProceeding Paper

Studying LF and HF Time Series to Characterize Cardiac Physiological Responses to Mental Fatigue

by Alexis Boffet, Veronique Deschodt Arsac and Eric Grivel

Eng. Proc. 2024, 68(1), 6; https://doi.org/10.3390/engproc2024068006 - 28 Jun 2024

Viewed by 107

Abstract

Heart rate variability (HRV) was largely used to evaluate psychophysiological status of Human at rest as well as during cognitive tasks, for both healthy subjects and patients. Among the approaches used for assessing cardiac autonomic control from HRV analysis, biomarkers such as the [...] Read more.

Heart rate variability (HRV) was largely used to evaluate psychophysiological status of Human at rest as well as during cognitive tasks, for both healthy subjects and patients. Among the approaches used for assessing cardiac autonomic control from HRV analysis, biomarkers such as the power in low and high frequencies (LF-HF) are often extracted from short-term recordings lasting 2 to 5 min. Although they correctly reflect the average psychophysiological state of a subject in situation, they fail to analyse cardiac autonomic control over time. For this reason, we suggest investigating the LF-HF biomarkers over time to identify mental fatigue and determine different physiological profiles. The following step consists in defining the set of parameters that characterise the LF-HF time series and that can be interpreted easily by the physiologists. In this work, polynomial models are considered to describe the trends of the LF-HF time series. The latter are then decomposed into decreasing (d) and increasing (i) parts. Finally, the proportion of the i parts of the polynomial trends of the LF and HF powers over time are combined with classically-used metrics to define individual profiles in response to mental fatigue. Full article

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9 pages, 11746 KiB

Open AccessProceeding Paper

Annual Runoff Forecasting through Bayesian Causality

by Santiago Zazo, Jose-Luis Molina, Carmen Patino-Alonso, Fernando Espejo and Juan Carlos García-Prieto

Eng. Proc. 2024, 68(1), 7; https://doi.org/10.3390/engproc2024068007 - 28 Jun 2024

Viewed by 101

Abstract

This contribution is focused on forecasting ability of Bayesian causality (BC) on annual runoff series. For that, the time series was synthetized through a Bayesian net, in which the probability propagation over the time was performed. The BC analytical ability identified the hidden [...] Read more.

This contribution is focused on forecasting ability of Bayesian causality (BC) on annual runoff series. For that, the time series was synthetized through a Bayesian net, in which the probability propagation over the time was performed. The BC analytical ability identified the hidden logic structure of the hydrological records that describes its overall behavior. This allowed us to quantify the runoff, through a novel dependence matrix, through two fractions, one conditional on time (temporally conditioned runoff) and one not (temporally nonconditioned runoff). This conditionality allowed the development of two predictive models for each fraction, analyzing their reliability under a double probabilistic and metrological approach. Full article

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7 pages, 2414 KiB

Open AccessProceeding Paper

Towards Resolving the Ambiguity in Low-Field, All-Optical Magnetic Field Sensing with High NV-Density Diamonds

by Ludwig Horsthemke, Jens Pogorzelski, Dennis Stiegekötter, Frederik Hoffmann, Ann-Sophie Bülter, Sarah Trinschek, Markus Gregor and Peter Glösekötter

Eng. Proc. 2024, 68(1), 8; https://doi.org/10.3390/engproc2024068008 - 1 Jul 2024

Viewed by 149

Abstract

In all-optical magnetic field sensing using nitrogen-vacancy-center-rich diamonds, an ambiguity in the range of 0–8 mT can be observed. We propose a way to resolve this ambiguity using the magnetic-field-dependent fluorescence lifetime. We therefore recorded the frequency response of the fluorescence upon modulation [...] Read more.

In all-optical magnetic field sensing using nitrogen-vacancy-center-rich diamonds, an ambiguity in the range of 0–8 mT can be observed. We propose a way to resolve this ambiguity using the magnetic-field-dependent fluorescence lifetime. We therefore recorded the frequency response of the fluorescence upon modulation of the excitation intensity in a frequency range of 1–

100 MHz

. The magnetic-field-dependent decay dynamics led to different response characteristics for magnetic fields below and above

3 mT

, allowing us to resolve the ambiguity. We used a physics-based model function to extract fit parameters, which we used for regression, and compared it to an alternative approach purely based on an artificial neural network. Full article

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8 pages, 942 KiB

Open AccessProceeding Paper

Modeling a Set of Variables with Different Attributes on a Quantitative Dependent Variable: An Application of Dichotomous Variables

by Gerardo Covarrubias and Xuedong Liu

Eng. Proc. 2024, 68(1), 9; https://doi.org/10.3390/engproc2024068009 - 1 Jul 2024

Viewed by 75

Abstract

This study outlines the methodology employed to model the relationship among a set of dichotomous variables, which represent attributes, on a nominal scale. The objective is to elucidate their influence on a quantitative dependent variable measured on a ratio scale. This approach allows [...] Read more.

This study outlines the methodology employed to model the relationship among a set of dichotomous variables, which represent attributes, on a nominal scale. The objective is to elucidate their influence on a quantitative dependent variable measured on a ratio scale. This approach allows for the quantification of the impact of these attributes and their significance in sha** the behavior of the entity possessing them. The resolution method employed for the estimation is ordinary least squares. However, it is crucial to note that interpreting the estimators in the resulting model requires a nuanced perspective, distinguishing it from the conventional interpretation of slope or rate of change in a classic model. To clarify, these estimators align with the average behavior of the dependent variable concerning binary characteristics, and the outcomes are consistent with the analysis of variance. Full article

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8 pages, 1495 KiB

Open AccessProceeding Paper

A Python Module for Implementing Cointegration Tests with Multiple Endogenous Structural Breaks

by Abdulnasser Hatemi-J and Alan Mustafa

Eng. Proc. 2024, 68(1), 10; https://doi.org/10.3390/engproc2024068010 - 2 Jul 2024

Viewed by 57

Abstract

Testing for long-run relationships between time series variables with short-run adjustments is an integral part of many empirical studies nowadays. Allowing for structural breaks in the estimations is a pertinent issue within this context. The purpose of this paper is to provide a [...] Read more.

Testing for long-run relationships between time series variables with short-run adjustments is an integral part of many empirical studies nowadays. Allowing for structural breaks in the estimations is a pertinent issue within this context. The purpose of this paper is to provide a consumer-friendly module that is created in Python for implementing three residuals-based cointegration tests with two unknown regime shifts. The timing of each shift is revealed endogenously. The software is easy to use via a Graphical User Interface (GUI). In addition to implementing cointegration tests, the software also estimates the underlying parameters along with the standard errors and the significance tests for the parameters. An application is also provided using real data to demonstrate how the software can be used. To our best knowledge, this is the first software component created in Python that implements cointegration tests with structural breaks. Full article

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8 pages, 1592 KiB

Open AccessProceeding Paper

Big Data Techniques Applied to Forecast Photovoltaic Energy Demand in Spain

by J. Tapia-García, L. G. B. Ruiz, D. Criado-Ramón and M. C. Pegalajar

Eng. Proc. 2024, 68(1), 11; https://doi.org/10.3390/engproc2024068011 - 3 Jul 2024

Viewed by 94

Abstract

Renewable energies play an important role in our society’s development, addressing the challenges presented by climate change. Specifically, in countries like Spain, technologies such as solar energy assume a crucial significance, enabling the generation of clean energy. This study addresses the critical need [...] Read more.

Renewable energies play an important role in our society’s development, addressing the challenges presented by climate change. Specifically, in countries like Spain, technologies such as solar energy assume a crucial significance, enabling the generation of clean energy. This study addresses the critical need to accurately predict photovoltaic (PV) energy demand in Spain. By using the data collected from the Spanish Electricity System, four models (Linear Regression, Random Forest, Recurrent Neural Network, and LightGBM) were implemented, with adaptations for Big Data. The LR model proved unsuitable, while the LGBM emerged as the most accurate and timely performer. The incorporation of Big Data adaptations amplifies the significance of our findings, highlighting the effectiveness of the LGBM in forecasting PV energy demand with both accuracy and efficiency. Full article

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10 pages, 6368 KiB

Open AccessProceeding Paper

Detecting Trend Turning Points in PS-InSAR Time Series: Slow-Moving Landslides in Province of Frosinone, Italy

by Ebrahim Ghaderpour, Benedetta Antonielli, Francesca Bozzano, Gabriele Scarascia Mugnozza and Paolo Mazzanti

Eng. Proc. 2024, 68(1), 12; https://doi.org/10.3390/engproc2024068012 - 3 Jul 2024

Viewed by 120

Abstract

Detecting slow-moving landslides is a crucial task for mitigating potential risk to human lives and infrastructures. In this research, Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) time series, provided by the European Ground Motion Service (EGMS), for the province of Frosinone in Italy [...] Read more.

Detecting slow-moving landslides is a crucial task for mitigating potential risk to human lives and infrastructures. In this research, Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) time series, provided by the European Ground Motion Service (EGMS), for the province of Frosinone in Italy are employed, and Sequential Turning Point Detection (STPD) is applied to them to estimate the dates when the displacement rates change. The estimated dates are classified based on the land cover/use of the province. Moreover, local precipitation time series are employed to investigate how precipitation rate changes might have triggered the landslides. Full article

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9 pages, 2175 KiB

Open AccessProceeding Paper

Measuring the Efficiency of Introducing Businesses’ Digitalization Elements over Time in Relation to Their Performance

by Jarmila Horváthová and Martina Mokrišová

Eng. Proc. 2024, 68(1), 13; https://doi.org/10.3390/engproc2024068013 - 3 Jul 2024

Viewed by 131

Abstract

The introduction of digitalization elements into the life of companies is significant in terms of achieving better economic results. The aim of the research was to determine the technical efficiency, as well as the change in efficiency and the technological change in the [...] Read more.

The introduction of digitalization elements into the life of companies is significant in terms of achieving better economic results. The aim of the research was to determine the technical efficiency, as well as the change in efficiency and the technological change in the digital transformation of companies in EU countries in relation to their performance. The Malmquist index was used to measure these parameters over time. The results of the research indicate the significance of the dynamic measurement of the efficiency of digital transformation. Interesting results also point to the importance of evaluating the efficiency of the use of already established elements, as well as evaluating the introduction of new technological changes. Full article

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9 pages, 915 KiB

Open AccessProceeding Paper

Evaluation of the University of Lagos Waste Generation Trend

by Charles A. Mbama, Austin Otegbulu, Iain Beverland and Tara K. Beattie

Eng. Proc. 2024, 68(1), 14; https://doi.org/10.3390/engproc2024068014 - 4 Jul 2024

Viewed by 76

Abstract

This study examines waste generation patterns at the University of Lagos (UoL), Nigeria, to inform decision-making towards improving the efficiency of the university’s management strategies in line with Sustainable Development Goal 12, target 12.5 to reduce waste generation through prevention, reduction, recycling, and [...] Read more.

This study examines waste generation patterns at the University of Lagos (UoL), Nigeria, to inform decision-making towards improving the efficiency of the university’s management strategies in line with Sustainable Development Goal 12, target 12.5 to reduce waste generation through prevention, reduction, recycling, and reuse by 2030. The moving average of the waste generation was studied using time series data. During October 2014 to October 2016 the UoL generated an average of 877.5 tons of waste every month, with the lowest observed value being 496.6 tons and the highest recorded value being 1250.5 tons. The trend result indicates a gradual decrease in the generation of waste over time. There is also a noticeable negative cyclical pattern with seasonal variations, where the highest generation point is observed in March and the lowest point is observed in June, particularly in the latter half of the second quarter, as time progresses. Although there is a reduction in the amount of waste generated over time, it is crucial to persist in evaluating diverse waste management strategies that could further reduce the amount of waste generated in the case study area. Full article

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9 pages, 834 KiB

Open AccessProceeding Paper

Modeling the Asymmetric and Time-Dependent Volatility of Bitcoin: An Alternative Approach

by Abdulnasser Hatemi-J

Eng. Proc. 2024, 68(1), 15; https://doi.org/10.3390/engproc2024068015 - 4 Jul 2024

Viewed by 103

Abstract

Volatility as a measure of financial risk is a crucial input for hedging, portfolio diversification, option pricing and the calculation of the value at risk. In this paper, we estimate the asymmetric and time-varying volatility for Bitcoin as the dominant cryptocurrency in the [...] Read more.

Volatility as a measure of financial risk is a crucial input for hedging, portfolio diversification, option pricing and the calculation of the value at risk. In this paper, we estimate the asymmetric and time-varying volatility for Bitcoin as the dominant cryptocurrency in the world market. A novel approach that explicitly separates the falling markets from the rising ones is utilized for this purpose. The empirical results have important implications for investors and financial institutions. Our approach provides a position-dependent measure of risk for Bitcoin. This is essential since the source of risk for an investor with a long position is the rising prices, while the source of risk for an investor with a short position is the falling prices. Thus, providing a separate risk measure in each case is expected to increase the efficiency of the underlying risk management in both cases compared to the existing methods in the literature. Full article

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7 pages, 1376 KiB

Open AccessProceeding Paper

Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks

by Ivan Gudelj, Mario Lovrić and Emmanuel Karlo Nyarko

Eng. Proc. 2024, 68(1), 16; https://doi.org/10.3390/engproc2024068016 - 4 Jul 2024

Viewed by 120

Abstract

This paper focuses on improving the prediction of the daily concentration of the pollutants, PM₁₀ and nitrogen oxides (NO, NO₂) in the air at urban monitoring sites using 1D convolutional neural networks (CNN). The results show that the 1D CNN [...] Read more.

This paper focuses on improving the prediction of the daily concentration of the pollutants, PM₁₀ and nitrogen oxides (NO, NO₂) in the air at urban monitoring sites using 1D convolutional neural networks (CNN). The results show that the 1D CNN model outperforms the other machine learning models (LSTM and Random Forest) in terms of the coefficients of determination and absolute errors. Full article

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16 pages, 2534 KiB

Open AccessProceeding Paper

Reservoir Neural Network Computing for Time Series Forecasting in Aerospace: Potential Applications to Predictive Maintenance

by Juan Manuel Rodríguez Riesgo and Juan Luis Cabrera Fernández

Eng. Proc. 2024, 68(1), 17; https://doi.org/10.3390/engproc2024068017 - 4 Jul 2024

Viewed by 128

Abstract

Coupling a reservoir neural network and a Grey Wolf optimization algorithm the system hyperparameters space is explored to find the configuration best suited to forecast the input sensor from the NASA CMAPSS dataset. In such a framework, the application to the problem of [...] Read more.

Coupling a reservoir neural network and a Grey Wolf optimization algorithm the system hyperparameters space is explored to find the configuration best suited to forecast the input sensor from the NASA CMAPSS dataset. In such a framework, the application to the problem of predictive maintenance is considered. The necessary requirements for the system to generate satisfactory predictions are established, with specific suggestions as to how a forecast can be improved through reservoir computing. The obtained results are used to determine certain common rules that improve the quality of the predictions and focus the optimization towards hyperparameter solutions that may allow for a faster approach to predictive maintenance. This research is a starting point to develop methods that could inform accurately on the remaining useful life of a component in aerospace systems. Full article

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7 pages, 730 KiB

Open AccessProceeding Paper

Foreign Exchange Forecasting Models: LSTM and BiLSTM Comparison

by Fernando García, Francisco Guijarro, Javier Oliver and Rima Tamošiūnienė

Eng. Proc. 2024, 68(1), 19; https://doi.org/10.3390/engproc2024068019 - 4 Jul 2024

Viewed by 78

Abstract

Knowledge of foreign exchange rates and their evolution is fundamental to firms and investors, both for hedging exchange rate risk and for investment and trading. The ARIMA model has been one of the most widely used methodologies for time series forecasting. Nowadays, neural [...] Read more.

Knowledge of foreign exchange rates and their evolution is fundamental to firms and investors, both for hedging exchange rate risk and for investment and trading. The ARIMA model has been one of the most widely used methodologies for time series forecasting. Nowadays, neural networks have surpassed this methodology in many aspects. For short-term stock price prediction, neural networks in general and recurrent neural networks such as the long short-term memory (LSTM) network in particular perform better than classical econometric models. This study presents a comparative analysis between the LSTM model and BiLSTM models. There is evidence for an improvement in the bidirectional model for predicting foreign exchange rates. In this case, we analyse whether this efficiency is consistent in predicting different currencies as well as the bitcoin futures contract. Full article

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336 KiB

Open AccessProceeding Paper

Extraction and Forecasting of Trends in Cases of Signal Rank Overestimation

by Nina Golyandina and Pavel Dudnik

Eng. Proc. 2024, 68(1), 20; https://doi.org/10.3390/engproc2024068020 - 5 Jul 2024

Abstract

Singular spectrum analysis allows automation for the extraction of trends of arbitrary shapes. Here, we study how the estimation of signal ranks influences the accuracy of trend extraction and forecasting. It is numerically shown that the trend estimates and their forecasting are slightly [...] Read more.

Singular spectrum analysis allows automation for the extraction of trends of arbitrary shapes. Here, we study how the estimation of signal ranks influences the accuracy of trend extraction and forecasting. It is numerically shown that the trend estimates and their forecasting are slightly changed if the signal rank is overestimated. If the trend is not of finite rank, the trend estimates are still stable, while forecasting may be unstable. The method of automation of the trend extraction includes an important step for improving the separability of time series components to avoid their mixture. However, the better the separability improvement, the larger the forecasting variability, since the noise components become separated and can be similar to trend ones. Full article

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10 pages, 2110 KiB

Open AccessProceeding Paper

Forecasting Stock Market Dynamics using Market Cap Time Series of Firms and Fluctuating Selection

by Hugo Fort

Eng. Proc. 2024, 68(1), 21; https://doi.org/10.3390/engproc2024068021 - 5 Jul 2024

Viewed by 96

Abstract

Evolutionary economics has been instrumental in explaining the nature of innovation processes and providing valuable heuristics for applied research. However, quantitative tests in this field remain scarce. A significant challenge is accurately estimating the fitness of companies. We propose the estimation of the [...] Read more.

Evolutionary economics has been instrumental in explaining the nature of innovation processes and providing valuable heuristics for applied research. However, quantitative tests in this field remain scarce. A significant challenge is accurately estimating the fitness of companies. We propose the estimation of the financial fitness of a company by its market capitalization (MC) time series using Malthusian fitness and the selection equation of evolutionary biology. This definition of fitness implies that all companies, regardless of their industry, compete for investors’ money through their stocks. The resulting fluctuating selection from market capitalization (FSMC) formula allows forecasting companies’ shares of total MC through this selection equation. We validate the method using the daily MC of public-owned Fortune 100 companies over the period 2000–2021. Full article

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