Forecasting with Hyper-Trees
<|reference_start|>Forecasting with Hyper-Trees: This paper introduces the concept of Hyper-Trees and offers a new direction in applying tree-based models to time series data. Unlike conventional applications of decision trees that forecast time series directly, Hyper-Trees are designed to learn the parameters of a target time series model. Our framework leverages the gradient-based nature of boosted trees, which allows us to extend the concept of Hyper-Networks to Hyper-Trees and to induce a time-series inductive bias to tree models. By relating the parameters of a target time series model to features, Hyper-Trees address the issue of parameter non-stationarity and enable tree-based forecasts to extend beyond their training range. With our research, we aim to explore the effectiveness of Hyper-Trees across various forecasting scenarios and to extend the application of gradient boosted decision trees outside their conventional use in time series modeling.<|reference_end|>
@article{märz2024forecasting,
    title={Forecasting with Hyper-Trees},
    author={Alexander M"arz, Kashif Rasul},
    journal={arXiv preprint arXiv:2405.07836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07836},
    primaryClass={cs.LG stat.ME}
}