Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting

Abstract

Deep neural networks such as Convolutional Neural Networks (CNNs) have been successfully applied to a wide variety of tasks, including time series forecasting. In this paper, we propose a novel approach for online deep CNN selection using saliency maps in the task of time series forecasting. We start with an arbitrarily set of different CNN forecasters with various architectures. Then, we outline a gradient-based technique for generating saliency maps with a coherent design to make it able to specialize the CNN forecasters across different regions in the input time series using a performance-based ranking. In this framework, the selection of the adequate model is performed in an online fashion and the computation of saliency maps responsible for the model selection is achieved adaptively following drift detection in the time series. In addition, the saliency maps can be exploited to provide suitable explanations for the reason behind selecting a specific model at a certain time interval or instant. An extensive empirical study on various real-world datasets demonstrates that our method achieves excellent or on par results in comparison to the state-of-the-art approaches as well as several baselines.