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11 pages, 727 KiB

Open AccessProceeding Paper

Evaluating Sales Forecasting Methods in Make-to-Order Environments: A Cross-Industry Benchmark Study

by Marius Syberg, Lucas Polley and Jochen Deuse

Comput. Sci. Math. Forum 2025, 11(1), 1; https://doi.org/10.3390/cmsf2025011001 - 25 Jul 2025

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Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in [...] Read more.

Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in MTO settings across three manufacturing sectors: electrical equipment, steel, and office supplies. A cross-industry benchmark assesses models such as ARIMA, Holt–Winters, Random Forest, LSTM, and Facebook Prophet. The evaluation considers error metrics (MAE, RMSE, and sMAPE) as well as implementation aspects like computational demand and interpretability. Special attention is given to data sensitivity and technical limitations typical in SMEs. The findings show that ML models perform well under high volatility and when enriched with external indicators, but they require significant expertise and resources. In contrast, simpler statistical methods offer robust performance in more stable or seasonal demand contexts and are better suited in certain cases. The study emphasizes the importance of transparency, usability, and trust in forecasting tools and offers actionable recommendations for selecting a suitable forecasting configuration based on context. By aligning technical capabilities with operational needs, this research supports more effective decision-making in data-constrained MTO environments. Full article

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10 pages, 775 KiB

Open AccessProceeding Paper

An Estimation of Risk Measures: Analysis of a Method

by Marta Ferreira and Liliana Monteiro

Comput. Sci. Math. Forum 2025, 11(1), 2; https://doi.org/10.3390/cmsf2025011002 - 25 Jul 2025

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Abstract

Extreme value theory comprises a set of techniques for inference at the tail of distributions, where data are scarce or non-existent. The tail index is the main parameter, with risk measures such as value at risk or expected shortfall depending on it. In [...] Read more.

Extreme value theory comprises a set of techniques for inference at the tail of distributions, where data are scarce or non-existent. The tail index is the main parameter, with risk measures such as value at risk or expected shortfall depending on it. In this study, we will analyze a method for estimating the tail index through a simulation study. This will allow for an application using real data including the estimation of the mentioned risk measures. Full article

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21 pages, 343 KiB

Open AccessProceeding Paper

Detecting Financial Bubbles with Tail-Weighted Entropy

by Omid M. Ardakani

Comput. Sci. Math. Forum 2025, 11(1), 3; https://doi.org/10.3390/cmsf2025011003 - 25 Jul 2025

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Abstract

This paper develops a novel entropy-based framework to quantify tail risk and detect speculative bubbles in financial markets. By integrating extreme value theory with information theory, I introduce the Tail-Weighted Entropy (TWE) measure, which captures how information scales with extremeness in asset price [...] Read more.

This paper develops a novel entropy-based framework to quantify tail risk and detect speculative bubbles in financial markets. By integrating extreme value theory with information theory, I introduce the Tail-Weighted Entropy (TWE) measure, which captures how information scales with extremeness in asset price distributions. I derive explicit bounds for TWE under heavy-tailed models and establish its connection to tail index parameters, revealing a phase transition in entropy decay rates during bubble formation. Empirically, I demonstrate that TWE-based signals detect crises in equities, commodities, and cryptocurrencies days earlier than traditional variance-ratio tests, with Bitcoin’s 2021 collapse identified weeks prior to the peak. The results show that entropy decay—not volatility explosions—serves as the primary precursor to systemic risk, offering policymakers a robust tool for preemptive crisis management. Full article

10 pages, 1431 KiB

Open AccessProceeding Paper

Time Series Forecasting for Touristic Policies

by Konstantinos Mavrogiorgos, Athanasios Kiourtis, Argyro Mavrogiorgou, Dimitrios Apostolopoulos, Andreas Menychtas and Dimosthenis Kyriazis

Comput. Sci. Math. Forum 2025, 11(1), 4; https://doi.org/10.3390/cmsf2025011004 - 30 Jul 2025

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Abstract

The formulation of touristic policies is a time-consuming process that consists of a wide range of steps and procedures. These policies are highly dependent on the number of tourists and visitors to an area to be as effective as possible. The estimation of [...] Read more.

The formulation of touristic policies is a time-consuming process that consists of a wide range of steps and procedures. These policies are highly dependent on the number of tourists and visitors to an area to be as effective as possible. The estimation of this number is not always easy to achieve, since there is a lack of the corresponding data (i.e., number of visitors per day). Hence, this estimation must be achieved by utilizing alternative data sources. To this end, in this paper, the authors propose a neural network architecture that is trained on waste management data to estimate the number of visitors and tourists in the highly touristic municipality of Vari-Voula-Vouliagmeni, Greece. Full article

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9 pages, 607 KiB

Open AccessProceeding Paper

Nonlinear Dynamic Inverse Solution of the Diffusion Problem Based on Krylov Subspace Methods with Spatiotemporal Constraints

by Luis Fernando Alvarez-Velasquez and Eduardo Giraldo

Comput. Sci. Math. Forum 2025, 11(1), 5; https://doi.org/10.3390/cmsf2025011005 - 30 Jul 2025

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Abstract

In this work, we propose a nonlinear dynamic inverse solution to the diffusion problem based on Krylov Subspace Methods with spatiotemporal constraints. The proposed approach is applied by considering, as a forward problem, a 1D diffusion problem with a nonlinear diffusion model. The [...] Read more.

In this work, we propose a nonlinear dynamic inverse solution to the diffusion problem based on Krylov Subspace Methods with spatiotemporal constraints. The proposed approach is applied by considering, as a forward problem, a 1D diffusion problem with a nonlinear diffusion model. The dynamic inverse problem solution is obtained by considering a cost function with spatiotemporal constraints, where the Krylov subspace method named the Generalized Minimal Residual method is applied by considering a linearized diffusion model and spatiotemporal constraints. In addition, a Jacobian-based preconditioner is used to improve the convergence of the inverse solution. The proposed approach is evaluated under noise conditions by considering the reconstruction error and the relative residual error. It can be seen that the performance of the proposed approach is better when used with the preconditioner for the nonlinear diffusion model under noise conditions in comparison with the system without the preconditioner. Full article

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16 pages, 2538 KiB

Open AccessProceeding Paper

Comparative Analysis of Temperature Prediction Models: Simple Models vs. Deep Learning Models

by Zibo Wang, Weiqi Zhang and Eugene Pinsky

Comput. Sci. Math. Forum 2025, 11(1), 6; https://doi.org/10.3390/cmsf2025011006 - 30 Jul 2025

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Abstract

Accurate and concise temperature prediction models have important applications in meteorological science, agriculture, energy, and electricity. This study aims to compare the performance of simple models and deep learning models in temperature prediction and explore whether simple models can replace deep learning models [...] Read more.

Accurate and concise temperature prediction models have important applications in meteorological science, agriculture, energy, and electricity. This study aims to compare the performance of simple models and deep learning models in temperature prediction and explore whether simple models can replace deep learning models in specific scenarios to save computing resources. Based on 37 years of daily temperature time series data from 10 cities from 1987 to 2024, the Simple Moving Average (SMA), Seasonal Average Method with Lookback Years (SAM-Lookback), and Long Short-Term Memory (LSTM) models are fitted to evaluate the accuracy of simple models and deep learning models in temperature prediction. The performance of different models is intuitively compared by calculating the RMSE and Percentage Error of each city. The results show that LSTM has higher accuracy in most cities, but the prediction results of SMA and LSTM are similar and perform equally well, while SAM-Lookback is relatively weak. Full article

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10 pages, 621 KiB

Open AccessProceeding Paper

An Autoregressive Moving Average Model for Time Series with Irregular Time Intervals

by Diana Alejandra Godoy Pulecio and César Andrés Ojeda Echeverri

Comput. Sci. Math. Forum 2025, 11(1), 8; https://doi.org/10.3390/cmsf2025011008 - 31 Jul 2025

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Abstract

This research focuses on the study of stochastic processes with irregularly spaced time intervals, which is present in a wide range of fields such as climatology, astronomy, medicine, and economics. Some studies have proposed irregular autoregressive (iAR) and moving average (iMA) models separately, [...] Read more.

This research focuses on the study of stochastic processes with irregularly spaced time intervals, which is present in a wide range of fields such as climatology, astronomy, medicine, and economics. Some studies have proposed irregular autoregressive (iAR) and moving average (iMA) models separately, and moving average autoregressive processes (iARMA) for positive autoregressions. The objective of this work is to generalize the iARMA model to include negative correlations. A first-order moving average autoregressive model for irregular discrete time series is presented, being an ergodic and strictly stationary Gaussian process. Parameter estimation is performed by Maximum Likelihood, and its performances are evaluated for finite samples through Monte Carlo simulations. The estimation of the autocorrelation function (ACF) is performed using the DCF (Discrete Correlation Function) estimator, evaluating its performance by varying the sample size and average time interval. The model was implemented on real data from two different contexts; the first one consists of the two-week measurement of star flares of the Orion Nebula in the development of the COUP and the second pertains to the measurement of sunspot cycles from 1860 to 1990 and their relationship to temperature variation in the northern hemisphere. Full article

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12 pages, 1701 KiB

Open AccessProceeding Paper

Analyzing and Classifying Time-Series Trends in Medals

by Minfei Liang, Yu Gao and Eugene Pinsky

Comput. Sci. Math. Forum 2025, 11(1), 9; https://doi.org/10.3390/cmsf2025011009 - 31 Jul 2025

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Abstract

Since the 19th century, the development of metallurgical technology has been influenced by various factors, such as materials, casting technology, political policies, and the economic development of different countries. This paper aims to analyze the time-series evolution trend in medal issues in different [...] Read more.

Since the 19th century, the development of metallurgical technology has been influenced by various factors, such as materials, casting technology, political policies, and the economic development of different countries. This paper aims to analyze the time-series evolution trend in medal issues in different countries and explore their historical and commemorative significance. Taking the characteristics of medal production places, types, compositions, diameters, weights, shapes, compositions, and thicknesses between 1850 and 2025 as indicators, data analysis methods such as time series, hierarchical cluster analysis (HCA), logistic regression, and random forests are used to study the process of medal development and influencing factors in the past 175 years. The results show that compared with the pre-World War II period, the weight and diameter of all medals of major countries changed significantly in different periods. Moreover, before and after World War II, there was a shift from traditional materials to cost-effective and convenient alternatives. Full article

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8 pages, 481 KiB

Open AccessProceeding Paper

Monitoring Multidimensional Risk in the Economy

by Alexander Tyrsin, Michail Gerasimov and Michael Beer

Comput. Sci. Math. Forum 2025, 11(1), 10; https://doi.org/10.3390/cmsf2025011010 - 31 Jul 2025

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Abstract

In economics, risk analysis is often associated with certain difficulties. These include the presence of several correlated risk factors, non-stationarity of economic processes, and small data samples. A mathematical model of multidimensional risk is described which satisfies the main features of processes in [...] Read more.

In economics, risk analysis is often associated with certain difficulties. These include the presence of several correlated risk factors, non-stationarity of economic processes, and small data samples. A mathematical model of multidimensional risk is described which satisfies the main features of processes in the economy. In the task of risk monitoring, we represent the analyzed factors as a set of correlated non-stationary time series. The method allows us to assess the risk at each moment using small data samples. For this purpose, risk factors are locally described in the form of parabolic or linear trends. An example of monitoring the risk of reducing the level of socio-economic development of Russia in 2000–2023 is considered. The monitoring results showed that the proposed multivariate risk model was generally sensitive to all the most significant economic shocks and adequately responded to them. Full article

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13 pages, 3038 KiB

Open AccessProceeding Paper

Inclusive Turnout for Equitable Policies: Using Time Series Forecasting to Combat Policy Polarization

by Natasya Liew, Sreeya R. K. Haninatha, Sarthak Pattnaik, Kathleen Park and Eugene Pinsky

Comput. Sci. Math. Forum 2025, 11(1), 11; https://doi.org/10.3390/cmsf2025011011 - 1 Aug 2025

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Abstract

Selective voter mobilization dominates U.S. elections, with campaigns prioritizing swing voters to win critical states. While effective for a short-term period, this strategy deepens policy polarization, marginalizes minorities, and undermines representative democracy. This paper investigates voter turnout disparities and policy manipulation using advanced [...] Read more.

Selective voter mobilization dominates U.S. elections, with campaigns prioritizing swing voters to win critical states. While effective for a short-term period, this strategy deepens policy polarization, marginalizes minorities, and undermines representative democracy. This paper investigates voter turnout disparities and policy manipulation using advanced time series forecasting models (ARIMA, LSTM, and seasonal decomposition). Analyzing demographic and geographic data, we uncover significant turnout inequities, particularly for marginalized groups, and propose actionable reforms to enhance equitable voter participation. By integrating data-driven insights with theoretical perspectives, this study offers practical recommendations for campaigns and policymakers to counter polarization and foster inclusive democratic representation. Full article

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11 pages, 391 KiB

Open AccessProceeding Paper

The Forecasting of Aluminum Prices: A True Challenge for Econometric Models

by Krzysztof Drachal and Joanna Jędrzejewska

Comput. Sci. Math. Forum 2025, 11(1), 13; https://doi.org/10.3390/cmsf2025011013 - 31 Jul 2025

Abstract

This paper explores the forecasting of aluminum prices using various predictive models dealing with variable uncertainty. A diverse set of economic and market indicators is considered as potential price predictors. The performance of models including LASSO, RIDGE regression, time-varying parameter regressions, LARS, ARIMA, [...] Read more.

This paper explores the forecasting of aluminum prices using various predictive models dealing with variable uncertainty. A diverse set of economic and market indicators is considered as potential price predictors. The performance of models including LASSO, RIDGE regression, time-varying parameter regressions, LARS, ARIMA, Dynamic Model Averaging, Bayesian Model Averaging, etc., is compared according to forecast accuracy. Despite the initial expectations that Bayesian dynamic mixture models would provide the best forecast accuracy, the results indicate that forecasting by futures prices and with Dynamic Model Averaging outperformed all other methods when monthly average prices are considered. Contrary, when monthly closing spot prices are considered, Bayesian dynamic mixture models happen to be very accurate compared to other methods, although beating the no-change method is still a hard challenge. Additionally, both revised and originally published macroeconomic time-series data are analyzed, ensuring consistency with the information available during real-time forecasting by mimicking the perspective of market players in the past. Full article

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10 pages, 1811 KiB

Open AccessProceeding Paper

Beyond the Hodrick Prescott Filter: Wavelets and the Dynamics of U.S.–Mexico Trade

by José Gerardo Covarrubias and Xuedong Liu

Comput. Sci. Math. Forum 2025, 11(1), 14; https://doi.org/10.3390/cmsf2025011014 - 1 Aug 2025

Abstract

This study analyzes the evolution of the Mexico–U.S. trade balance as a seasonally adjusted time series, comparing the Hodrick–Prescott (HP) filter and wavelet analysis. The HP filter allowed the trend and cycle to be extracted from the series, while wavelets decomposed the information [...] Read more.

This study analyzes the evolution of the Mexico–U.S. trade balance as a seasonally adjusted time series, comparing the Hodrick–Prescott (HP) filter and wavelet analysis. The HP filter allowed the trend and cycle to be extracted from the series, while wavelets decomposed the information into different time scales, revealing short-, medium-, and long-term fluctuations. The results show that HP provides a simplified view of the trend, while wavelets more accurately capture key events and cyclical dynamics. It is concluded that wavelets offer a more robust tool for studying the volatility and persistence of economic shocks in bilateral trade. Full article

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11 pages, 3342 KiB

Open AccessProceeding Paper

Fundamentals of Time Series Analysis in Electricity Price Forecasting

by Ciaran O’Connor, Andrea Visentin and Steven Prestwich

Comput. Sci. Math. Forum 2025, 11(1), 16; https://doi.org/10.3390/cmsf2025011016 - 11 Aug 2025

Abstract

Time series forecasting is a cornerstone of decision-making in energy and finance, yet many studies fail to rigorously analyse the underlying dataset characteristics, leading to suboptimal model selection and unreliable outcomes. This paper addresses these shortcomings by presenting a comprehensive framework that integrates [...] Read more.

Time series forecasting is a cornerstone of decision-making in energy and finance, yet many studies fail to rigorously analyse the underlying dataset characteristics, leading to suboptimal model selection and unreliable outcomes. This paper addresses these shortcomings by presenting a comprehensive framework that integrates fundamental time series diagnostics—stationarity tests, autocorrelation analysis, heteroscedasticity, multicollinearity, and correlation analysis—into forecasting workflows. Unlike existing studies that prioritise pre-packaged machine learning and deep learning methods, often at the expense of interpretable statistical benchmarks, our approach advocates for the combined use of statistical models alongside advanced machine learning methods. Using the Day-Ahead Market dataset from the Irish electricity market as a case study, we demonstrate how rigorous statistical diagnostics can guide model selection, improve interpretability, and improve forecasting accuracy. This work offers a novel, integrative methodology that bridges the gap between statistical rigour and modern computational techniques, improving reliability in time series forecasting. Full article

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