Online Adaptive Local Interpretable Tree Ensembles for Time Series Forecasting
Thanks to their inherent interpretability, tree models are widely utilized in various learning tasks, including time series forecasting. However, single tree models often suffer from overfitting, limiting their applicability to real-world scenarios. To address this issue, ensembles of tree models are commonly employed. Yet, ensemble construction must account for the dynamic nature of time series, which can be subject to significant changes and the so-called concept drift phenomenon. In this paper, we develop local tree ensembles by specializing individual trees across specific regions in the input time series. We select the trees based on the most recent local pattern and manage their interdependence explicitly to ensure diversity in the ensemble. This is achieved through a carefully designed weighting schema. The trees are updated in an informed manner over time following concept drift detection in the time series. In addition, our approach supports explainability in forecasting tasks. Through extensive empirical evaluation on diverse real-world datasets, our method demonstrates comparable or superior performance to state-of-the-art approaches and several baseline methods.
- Published in:
IEEE International Conference on Data Mining Workshops - Type:
Inproceedings - Authors:
Saadallah, Amal - Year:
2024
Citation information
Saadallah, Amal: Online Adaptive Local Interpretable Tree Ensembles for Time Series Forecasting, IEEE International Conference on Data Mining Workshops, 2024, IEEE Computer Society, Saadallah.2024b,
@Inproceedings{Saadallah.2024b,
author={Saadallah, Amal},
title={Online Adaptive Local Interpretable Tree Ensembles for Time Series Forecasting},
booktitle={IEEE International Conference on Data Mining Workshops},
publisher={IEEE Computer Society},
year={2024},
abstract={Thanks to their inherent interpretability, tree models are widely utilized in various learning tasks, including time series forecasting. However, single tree models often suffer from overfitting, limiting their applicability to real-world scenarios. To address this issue, ensembles of tree models are commonly employed. Yet, ensemble construction must account for the dynamic nature of time series,...}}