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arXiv Machine Learning By Kurt Butler, Damian Machlanski, Michael P. J. Camilleri, Zhihua Liu, Panagiotis Dimitrakopoulos, Feng Chen, Ricardo Silva, Sotirios A. Tsaftaris

One Thing Leads to Another: The Essentials of Time Series Causal Discovery

Read the original on arXiv Machine Learning →

The paper "One Thing Leads to Another: The Essentials of Time Series Causal Discovery" offers a unified tutorial on detecting causation between time series, tracing its origins from signal processing and outlining its evolution across statistics, dynamical systems, and machine learning. It reviews key principles, limitations, assumptions, and evaluation methods of modern discovery techniques, and highlights their growing importance in fields such as agriculture, Earth science, healthcare, and AI. A companion repository provides code packages and resources to support readers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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