ShapTime: A General XAI Approach for Explainable Time Series Forecasting

The application of Explainable AI (XAI) in time series forecasting has gradually attracted attention, given the widespread implementation of machine learning and deep learning. ShapTime - A general XAI approach based on Shapley Value specially developed for explainable time series forecasting, which can explore more plentiful information in the temporal dimension, instead of only roughly applying traditional XAI approaches to time series forecasting as in previous works. Its novel components include: (1) It provides the relatively stable explanation in the temporal dimension, that is, the explanation result can reflect the importance of time itself, which is more suitable for time series forecasting than traditional XAI approaches; (2) It builds the practical application scenario of XAI - improving forecasting performance guided by explanation results. This is distinctly different from previous works, which only present the results of XAI as the demonstration of innovation. Eventually, in five real-world datasets, ShapTime’s average performance improvements for Boosting, RNN-based and Bi-RNN-based reached 18, 20 and 35 \(\%\) , respectively.