"Time Series Analysis: Data, Methods, and Applications" ed. by Chun-Kit Ngan

This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

This book is divided into three sections and each section includes two chapters.
Section 1 discusses analyzing multivariate and fuzzy time series.
Section 2 focuses on developing deep neural networks for time series forecasting and classification.
Section 3 describes solving real-world domain-specific problems using time series techniques.

Contents
1.Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series
2.Fuzzy Forecast Based on Fuzzy Time Series
3.Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting
4.CNN Approaches for Time Series Classification
5.Forecasting Shrimp and Fish Catch in Chilika Lake over Time Series Analysis
6.Using Gray-Markov Model and Time Series Model to Predict Foreign Direct Investment Trend for Supporting China’s Economic Development