Abstract

Abstract
Time series forecasting is pivotal across industries, as it fosters data-driven decision-making, increasing the chances of successful outcomes. Yet, certain instances that feature adverse characteristics, may lead models to manifest stress through decreases in performance (e.g., large errors). Hence, the ability to preemptively identify such cases, while establishing their root causes, would be advantageous to elevate the understanding of forecasting processes, informing users about the trustworthiness of predictions. Hence, we propose MASTFM, a method based on meta-learning that leverages statistical characteristics of input time series, and estimations of forecasting performance from model outputs, to build a metamodel that learns conditions for stress. Given that such occurrences are naturally rare, data augmentation is employed to ensure balance during training. Moreover, SHapley Additive exPlanations (SHAP) are used to explain how features impact forecasting behaviour. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract
Time series forecasting is an important tool for planning and decision-making. Considering this, several forecasting algorithms can be used, with results depending on the characteristics of the time series. The recommendation of the most suitable algorithm is a frequent concern. Metalearning has been successfully used to recommend the best algorithm for a time series analysis task. Additionally, it has been shown that decomposition methods can lead to better results. Based on previously published studies, in the experiments carried out, time series components were used. This work proposes and empirically evaluates METAFORE, a new time series forecasting approach that uses seasonal trend decomposition with Loess and metalearning to recommend suitable algorithms for time series forecasting combinations. Experimental results show that METAFORE can obtain a better predictive performance than single models with statistical significance. In the experiments, METAFORE also outperformed models widely used in the state-of-the-art, such as the long short-term memory neural network architectures, in more than 70%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$70\%$$\end{document} of the time series tested. Finally, the results show that the joint use of metalearning and time series decomposition provides a competitive approach to time series forecasting.

Abstract
In the study, a new method for analyzing Electrocardiogram (ECG) signals is suggested, which is vital for detecting and treating heart diseases. The technique focuses on improving ECG signal classification, particularly in identifying different heart conditions like arrhythmias and myocardial infarctions. An enhanced version of the differential evolution (DE) algorithm integrated with neural networks is leveraged to classify these signals effectively. The process starts with preprocessing and extracting key features from ECG signals. These features are then processed by a multi-layer perceptron (MLP), a common neural network for ECG analysis. However, traditional MLP training methods have limitations, such as getting trapped in suboptimal solutions. To overcome this, an advanced DE algorithm is used, incorporating a partition-based strategy, opposition-based learning, and local search mechanisms. This improved DE algorithm optimizes the MLP by fine-tuning its weights and biases, using them as starting points for further refinement by the Gradient Descent with Momentum (GDM) local search algorithm. Extensive experiments demonstrate that this novel training approach yields better results than the traditional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper compares the performance of Simulated Annealing and Tabu Search meta-heuristics in addressing a parallel machine scheduling problem aimed at minimizing weighted earliness, tardiness, total flowtime, and machine deterioration costs-a multi-objective optimization problem. The problem is transformed into a single-objective problem using weighting and weighting relative distance methods. Four scenarios, varying in the number of jobs and machines, are created to evaluate these metaheuristics. Computational experiments indicate that Simulated Annealing consistently yields superior solutions compared to Tabu Search in scenarios with lower dimensions despite longer run times. Conversely, Tabu Search performs better in higher-dimensional scenarios. Furthermore, it is observed that solutions generated by different weighting methods exhibit similar performance. © 2025 The Author(s).