Applied Time Series Analysis and Forecasting with Python

- 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.

Author: Changquan Huang, Alla Petukhina
Publisher: Springer
Published: 10/20/2022
Pages: 372
Binding Type: Hardcover
Weight: 1.57lbs
Size: 9.21h x 6.14w x 0.88d
ISBN13: 9783031135835
ISBN10: 3031135830
BISAC Categories:
- Mathematics | Probability & Statistics | General
- Computers | Mathematical & Statistical Software
- Business & Economics | Econometrics

About the Author
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.