Applied Time Series Analysis and Forecasting with Python

Changquan Huang, Alla Petukhina (Autoren)

Buch | Hardcover

X, 372 Seiten

2022 | 1st ed. 2022
Springer International Publishing (Verlag)
978-3-031-13583-5 (ISBN)

Artikel merken

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.

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-Universitat 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.

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.

Erscheinungsdatum	21.10.2022
Reihe/Serie	Statistics and Computing
Zusatzinfo	X, 372 p. 249 illus., 246 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	695 g
Themenwelt	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
	Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
Schlagworte	ARMA and ARIMA • Artificial Intelligence • Big data analysis • Data Science • Data Visualization • Financial Time Series • Forecasting • Machine Learning for Time Series • Markov Switching Models • multivariate time series • Nonstationary Time Series • Python • state space models • stationary time series • Time Series Analysis
ISBN-10	3-031-13583-0 / 3031135830
ISBN-13	978-3-031-13583-5 / 9783031135835
Zustand	Neuware