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. With Modern Time Series Forecasting with Python, Second Edition, you'll master cutting-edge deep learning architectures and advanced statistical techniques alongside classic methods like ARIMA and exponential smoothing. Learn the fundamentals from preprocessing, feature engineering, and evaluation to applying powerful machine and deep learning models, including ensemble and global methods.

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.

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Spis treści

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

• 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