Markov-switching decision trees

Research output: Contribution to journalJournal articleResearchpeer-review

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.

Original languageEnglish
JournalAStA Advances in Statistical Analysis
Publication statusE-pub ahead of print - 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

    Research areas

  • Decision trees, EM algorithm, Hidden Markov models, Time series modeling

ID: 395079319