E-book details

Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition

Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition

Manu Joseph, Jeffrey Tackes, Christoph Bergmeir

Ebook
Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.
Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.
This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.
  • 1. Introducing Time Series
  • 2. Acquiring and Processing Time Series Data
  • 3. Analyzing and Visualizing Time Series Data
  • 4. Setting a Strong Baseline Forecast
  • 5. Time Series Forecasting as Regression
  • 6. Feature Engineering for Time Series Forecasting
  • 7. Target Transformations for Time Series Forecasting
  • 8. Forecasting Time Series with Machine Learning Models
  • 9. Ensembling and Stacking
  • 10. Global Forecasting Models
  • 11. Introduction to Deep Learning
  • 12. Building Blocks of Deep Learning for Time Series
  • 13. Common Modeling Patterns for Time Series
  • 14. Attention and Transformers for Time Series
  • 15. Strategies for Global Deep Learning Forecasting Models
  • 16. Specialized Deep Learning Architectures for Forecasting
  • 17. Probabilistic Forecasting and More
  • 18. Multi-Step Forecasting
  • 19. Evaluating Forecast Errors—A Survey of Forecast Metrics
  • 20. Evaluating Forecasts – Validation Strategies
  • Title: Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition
  • Author: Manu Joseph, Jeffrey Tackes, Christoph Bergmeir
  • Original title: Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition
  • ISBN: 9781835883198, 9781835883198
  • Date of issue: 2024-10-31
  • Format: Ebook
  • Item ID: e_3y51
  • Publisher: Packt Publishing