Online Explainable Ensemble of Tree Models Pruning for Time Series Forecasting

Tree-based models are commonly used in time series forecasting due to their inherent interpretability, which makes them preferable to more complex black-box models. However, simple tree-based models are prone to overfitting, limiting their applicability in real-world scenarios. Ensembles of tree-based models are employed to mitigate this, but ensemble pruning is challenging, especially in the presence of dynamic time series data and concept drift. In this paper, we use TreeSHAP, a tree-specific explainability tool, to perform online tree-based ensemble pruning that adapts dynamically to changes in the time series, addressing the concept drift issue. Empirical evaluations on real-world time series datasets demonstrate that our method performs on par with or better than state-of-the-art techniques. In future research, we plan to automate the determination of the optimal number of clusters for ensemble pruning by leveraging ensemble properties like diversity, accuracy, and stability. This automation aims to enhance both the flexibility and explainability of the model selection process. Given that this work is in its early stages, we seek feedback and collaboration with experts to create a robust and explainable framework for ensemble-based time series forecasting.