Applied Time Series Analysis and Forecasting with Python (Statistics and Computing)

Book Details

• Publisher: Springer

• Author: Changquan Huang

• Language: English

• Edition: 1st Edition (2022)

• ISBN: 9783031135866

• Pages: 372

• Cover: Paperback

• Dimensions:

  • 9.3 x 6.1 x 1.0 inches
    

About The Book

This textbook, Time Series Analysis and Forecasting with Python by Changquan Huang, provides a comprehensive guide to time series analysis and forecasting. It covers a wide range of statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH, as well as state space and Markov switching models. These models are essential for analyzing (non)stationary, multivariate, and financial time series data.

In addition to these traditional models, the book also introduces modern machine learning techniques and addresses the challenges faced in time series forecasting. The unique strength of this book lies in its seamless integration of time series theory with Python programming. Readers will not only learn key concepts but also practice implementing them through Python code, which is essential for solving real-world data science problems.

The data-driven approach allows readers, particularly students of statistics, economics, and data science, to visualize and interpret both raw data and computed results effectively. Whether you're a student or an industry professional, this book offers valuable insights into analyzing and modeling time series data, making it a must-have resource for anyone looking to use Python in the field of data science and artificial intelligence.