Advancing explainability in multivariate time series classification through generative models - Meng, Han (2024)
As Artificial Intelligence (AI) techniques become increasingly integrated in various domains, concerns about their interpretability have increased. This has catalysed the development of eXplainable AI (XAI), which seeks to make AI systems more understandable to humans. However, most XAI research has focused on tabular data, images, or language models, with time series models receiving limited attention despite their pervasiveness. This thesis aims to advance XAI specifically within the context of Multivariate Time Series Classification (MTSC) problems, providing varied explanation types to meet diverse user needs.