Time Series Forecasting in Python
Author: Marco Peixeiro
Publisher Finelybook 出版社：Manning (August 30, 2022)
pages 页数：400 pages
Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.
Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.
Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You’ll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. Author: the time you’re done, you’ll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Time Series Forecasting in Python MEAP VO3
Chapter 1: Understanding time series forecasting
Chapter 2: A naive prediction of the future Chapter 3: Going on a random walk
Chapter 4: Modeling a moving average process Chapter 5: Modeling an autoregressive process Chapter 6: Modeling complex time series Chapter 7: Forecasting non-stationary time series
Chapter 8: Accounting for seasonality Chapter 9: Adding external variables to our model