Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition DOI Creative Commons
A. Pagliaro, Antonio Alessio Compagnino, Pierluca Sangiorgi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3165 - 3165

Published: March 14, 2025

Time series analysis and pattern recognition are cornerstones for innovation across diverse domains. In finance, these techniques enable market prediction risk assessment. Astrophysicists use them to detect various phenomena analyze data. Environmental scientists track ecosystem changes pollution patterns, while healthcare professionals monitor patient vitals disease progression. Transportation systems optimize traffic flow predict maintenance needs. Energy providers balance grid loads forecast consumption. Climate model atmospheric extreme weather events. Cybersecurity experts identify threats through anomaly detection in network patterns. This editorial introduces this Special Issue, which explores state-of-the-art AI machine learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Transformers, ensemble methods, AutoML frameworks. We highlight innovative applications data-driven astrophysical event reconstruction, cloud masking, monitoring. Recent advancements feature engineering, unsupervised frameworks Transformer-based time forecasting demonstrate the potential of technologies. The papers collected Issue showcase how integrating domain-specific knowledge with computational innovations provides a pathway achieving higher accuracy scientific disciplines.

Language: Английский

Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition DOI Creative Commons
A. Pagliaro, Antonio Alessio Compagnino, Pierluca Sangiorgi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3165 - 3165

Published: March 14, 2025

Time series analysis and pattern recognition are cornerstones for innovation across diverse domains. In finance, these techniques enable market prediction risk assessment. Astrophysicists use them to detect various phenomena analyze data. Environmental scientists track ecosystem changes pollution patterns, while healthcare professionals monitor patient vitals disease progression. Transportation systems optimize traffic flow predict maintenance needs. Energy providers balance grid loads forecast consumption. Climate model atmospheric extreme weather events. Cybersecurity experts identify threats through anomaly detection in network patterns. This editorial introduces this Special Issue, which explores state-of-the-art AI machine learning (ML) techniques, including Long Short-Term Memory (LSTM) networks, Transformers, ensemble methods, AutoML frameworks. We highlight innovative applications data-driven astrophysical event reconstruction, cloud masking, monitoring. Recent advancements feature engineering, unsupervised frameworks Transformer-based time forecasting demonstrate the potential of technologies. The papers collected Issue showcase how integrating domain-specific knowledge with computational innovations provides a pathway achieving higher accuracy scientific disciplines.

Language: Английский

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