Overview
- 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
Part of the book series: Statistics and Computing (SCO)
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About this book
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.
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Keywords
- Time Series Analysis
- Python
- Forecasting
- Big Data Analysis
- Data Visualization
- Machine Learning for Time Series
- Artificial Intelligence
- Stationary Time Series
- Nonstationary Time Series
- Multivariate Time Series
- Financial Time Series
- State Space Models
- Markov Switching Models
- ARMA and ARIMA
- Data Science
Table of contents (10 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Applied Time Series Analysis and Forecasting with Python
Authors: Changquan Huang, Alla Petukhina
Series Title: Statistics and Computing
DOI: https://doi.org/10.1007/978-3-031-13584-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-13583-5Published: 20 October 2022
Softcover ISBN: 978-3-031-13586-6Published: 20 October 2023
eBook ISBN: 978-3-031-13584-2Published: 19 October 2022
Series ISSN: 1431-8784
Series E-ISSN: 2197-1706
Edition Number: 1
Number of Pages: X, 372
Number of Illustrations: 3 b/w illustrations, 246 illustrations in colour
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Econometrics, Python, Machine Learning, Statistics for Business, Management, Economics, Finance, Insurance