time-series

We introduce an EXplainable artificial intelligence method targeting Time-series model based on Evolutionary Algorithm (ExTea). ExTea conceptualizes explanations as evolving individuals and employs an innovative pyramidal structure for optimizing potential explanations, categorized into newborn, tested, and elite stages.

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. 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.