Applied Time Series Analysis and Forecasting with Python

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equallyappeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

Presents methods and applications of time series analysis and forecasting using Python Addresses common statistical methods as well as modern machine learning procedures Provides a step-by-step demonstration of the Python code, and exercises for each chapter

Auteur

Changquan Huang is an Associate Professor at the Department of Statistics and Data Science, School of Economics, Xiamen University (XMU), China. He obtained his PhD in Statistics from The Chinese University of Hong Kong. For over 18 years, he has taught the course Time Series Analysis at XMU. He has authored and translated monographs in Chinese, including Bayesian Statistics with R (Tsinghua University Press 2017) and Time Series and Financial Data Analysis (China Statistics Press 2004). His research interests now cover applied statistics and artificial intelligence methods for time series.Alla Petukhina is a Lecturer at the School of Computing, Communication and Business, HTW Berlin, Germany. She was a postdoctoral researcher at the School of Business and Economics at the Humboldt-Universität zu Berlin, where she obtained her PhD in Statistics in 2018. Her research interests include asset allocation strategies, regression shrinkage techniques, quantiles and expectiles, history of statistics and investment strategies with crypto-currencies.

Contenu

1. Time Series Concepts and Python.- 2. Exploratory Time Series Data Analysis.- 3. Stationary Time Series Models.- 4. ARMA and ARIMA Modeling and Forecasting.- 5. Nonstationary Time Series Models.- 6. Financial Time Series and Related Models.- 7. Multivariate Time Series Analysis.- 8. State Space Models and Markov Switching Models.- 9. Nonstationarity and Cointegrations.- 10. Modern Machine Learning Methods for Time Series Analysis.