Time Series Analysis in Machine Learning: A Pedagogical Review
A new arXiv preprint offers a comprehensive review of time series analysis techniques from a machine learning perspective, spanning classical statistical models to modern deep learning approaches. The chapter, authored by Antonio Pagliaro, was invited for an upcoming Springer edited volume on machine learning in astrophysics and cosmology expected in 2026. It aims to bridge foundational theory and practical application across domains including astronomy, weather forecasting, and finance.
Submitted to arXiv on June 10, 2026, the preprint presents a pedagogical survey of time series analysis methods intended for researchers in astrophysics and cosmology, where temporal datasets are especially prevalent. The review begins with foundational concepts such as stationarity, autocorrelation, and seasonality, then progresses through classical statistical frameworks including autoregressive models, moving averages, ARIMA, exponential smoothing, and state-space models. It subsequently covers machine learning approaches such as feature-based regression, tree-based ensemble methods, hidden Markov models, and Gaussian processes, before addressing deep learning architectures including recurrent neural networks, convolutional networks, and transformers. Examples drawn from multiple scientific and applied domains are used throughout to highlight shared principles across fields. The chapter is an invited contribution to 'Machine Learning Techniques for Astrophysics and Cosmology,' edited by Cosimo Bambi, Vinay Kashyap, Swarnim Shashank, and Naoki Yoshida, and published by Springer Singapore. As a preprint, it has not yet undergone formal peer review, though the invitation for an edited academic volume implies a degree of editorial vetting.
What's missing
As a preprint chapter rather than an original research study, the primary limitations are scope-related: the review does not appear to benchmark the discussed methods against one another empirically. No information is provided about the author's institutional affiliation or potential conflicts of interest.
What different sources said
- arXiv stat.MLCenter
Time Series Analysis in Machine Learning
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