AI for Time Series: Volume 2: Building Robust and Generalizable Models

This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift and foundation models.It offers an in-depth look at cutting-edge techniques and methodologies, using advance algorithms that are transforming time series analysis across industries.The authors highlight the use AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations.This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection.The authors also