Applied Time Series Analysis and Forecasting with Python

Author:   Changquan Huang ,  Alla Petukhina
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2022
ISBN:  

9783031135866


Pages:   372
Publication Date:   20 October 2023
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Applied Time Series Analysis and Forecasting with Python


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Author:   Changquan Huang ,  Alla Petukhina
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2022
Weight:   0.587kg
ISBN:  

9783031135866


ISBN 10:   3031135865
Pages:   372
Publication Date:   20 October 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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Author Information

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.

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