Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision

Abstract

Various domains such as computer vision, natural language processing, and time series analysis have extensively applied machine learning algorithms in recent years. This chapter will discuss the research and applications of the interpretable and explainable algorithms in this domain. We will start with a time series algorithm survey, starting from traditional interpretable statistical models to modern deep learning algorithms. Next, we discuss NLP applications and the role of interpretability. Finally, we cover computer vision and how explainability has been a focus of considerable research. We will present a case study in each domain where the reader can get practical and real-world insights.