Time Series Forecasting via Reinforcement-Learning-Based Model Combination

Time series data permeates our daily existence and has been recognized as of significant importance for many sectors, such as energy, transportation, telecommunication, and health care. Ensemble learning stands as a prevalent technique in time series forecasting, adept at handling dynamic data distributions and enhancing robustness. Nevertheless, the determination of model combination weights for computing ensemble outputs remains an unresolved issue. In this endeavour, we introduce a comprehensive reinforcement-learning-based approach to tackle this challenge. Moreover, our method remains agnostic to the base models within the ensemble and exhibits versatility across a spectrum of time series forecasting tasks, thereby holding promise for application across diverse real-world scenarios. In this work, we extensively test the performance of our method on real-world datasets. Experimental results show that our proposed method achieves an average 6.2% forecasting accuracy improvement over all other single baseline methods.