Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting

Understand

Time series forecasting is an important yet challenging task.

• Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models.
• Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider the interpretation in the feature dimension while ignoring the crucial temporal dimension.
• In this paper, we present the series saliency framework for temporal interpretation for multivariate time series forecasting, which considers the forecasting interpretation in both feature and temporal dimensions.