Time Series Analysis in Machine Learning: A Pedagogical Review
A comprehensive review article covers time series analysis techniques from a machine learning perspective, spanning classical statistical models and modern deep learning approaches. The paper emphasizes foundational concepts like stationarity and autocorrelation before progressing to advanced methods including neural networks and transformers. This pedagogical approach is valuable for researchers across domains such as astrophysics, weather forecasting, and finance who work with temporal data.
The arXiv paper provides a structured overview of time series analysis in machine learning, beginning with fundamental concepts and progressing through classical statistical approaches to contemporary deep learning methods. It covers traditional models such as ARIMA and exponential smoothing alongside modern techniques including recurrent neural networks, convolutional networks, Gaussian processes, and transformers. The authors illustrate concepts with examples from multiple domains—astronomy, weather forecasting, and finance—to demonstrate the universal applicability of these principles. The review is designed to give readers both theoretical grounding and practical context for applying machine learning to time series problems in their own research.
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Time Series Analysis in Machine Learning
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