Online Explainable Model Selection for Time Series Forecasting
Amal Saadallah · 2023
Several machine learning models have been used to tackle time series forecasting. However, it is generally accepted that none of them is universally valid for every application and over time. Therefore, adequate and adaptive real-time model selection is often required to cope with the time-evolving nature of time series and the fact that models have specific Regions of Competence (RoCs) across the time series. In this paper, we perform an online single model selection for time series forecasting by using an adaptive clustering method to compute the RoCs of candidate models. This method can be extended to ensemble base models selection by combining clustering with a rank-based approach. In this framework, the appropriate model(s) is selected online, and the RoCs responsible for model selection update is done adaptively in an informed manner following concept drift detection in the RoCs’ structure. Moreover, the computed RoCs can be used to provide suitable explanations for the reason for selecting certain model(s) in a certain time interval or instant. Since the RoCs are computed independently of the family of forecasting models in question, the explanations we provide are model-agnostic. An extensive empirical study on various real-world datasets shows that our method achieves excellent or on-par results compared to state-of-the-art approaches and various baselines.