Markov-switching decision trees

Timo Adam (), Marius Ötting () and Rouven Michels ()
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Timo Adam: University of Copenhagen
Marius Ötting: Bielefeld University
Rouven Michels: Bielefeld University

AStA Advances in Statistical Analysis, 2024, vol. 108, issue 2, No 11, 476 pages

Abstract: 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.

Keywords: Decision trees; EM algorithm; Hidden Markov models; Time series modeling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10182-024-00501-6

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