State Graph Based Explanation Approach for Black-Box Time Series Model

In recent years, there has been a growing trend in the utilization of Artificial Intelligence (AI) technology to construct human-centered systems that are based on implicit time series information, ranging from contextual recommendations on smartwatches to human activity recognition on production workshop. Despite the advantages of these systems, the opaqueness and unpredictability of these systems for users has elicited concerns. To mitigate these issues, time-series explanation methods have been proposed. However, existing methods only focus on the segment importance of the instance to be explained and ignore its chronological nature. In this paper, we propose a novel explanation method named State-graph Based eXplanable Artificial Intelligent (SBXAI), which exhibits the sequential relationship between time periods through directed circular graphs while emphasizing the importance of each time period in an instance. Our proposed method was evaluated on 20 time-series datasets, and the results showed that the explanations provided by SBXAI are consistent with the behavior of the AI model in making predictions.