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Markov-switching decision trees

Author

Listed:
  • Timo Adam

    (University of Copenhagen
    Bielefeld University)

  • Marius Ötting

    (Bielefeld University)

  • Rouven Michels

    (Bielefeld University)

Abstract

Decision trees constitute a simple yet powerful and interpretable machine learning tool. While tree-based methods are designed only for cross-sectional data, we propose an approach that combines decision trees with time series modeling and thereby bridges the gap between machine learning and statistics. In particular, we combine decision trees with hidden Markov models where, for any time point, an underlying (hidden) Markov chain selects the tree that generates the corresponding observation. We propose an estimation approach that is based on the expectation-maximisation algorithm and assess its feasibility in simulation experiments. In our real-data application, we use eight seasons of National Football League (NFL) data to predict play calls conditional on covariates, such as the current quarter and the score, where the model’s states can be linked to the teams’ strategies. R code that implements the proposed method is available on GitHub.

Suggested Citation

  • Timo Adam & Marius Ötting & Rouven Michels, 2024. "Markov-switching decision trees," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(2), pages 461-476, June.
    • Handle: RePEc:spr:alstar:v:108:y:2024:i:2:d:10.1007_s10182-024-00501-6
    DOI: 10.1007/s10182-024-00501-6
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    References listed on IDEAS

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    Cited by:

    1. Benjamin Säfken & David Rügamer, 2024. "Editorial special issue: Bridging the gap between AI and Statistics," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(2), pages 225-229, June.

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