We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.

This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.

By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.

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Spis treści

Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning 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. Multi-Step Forecasting
• 18. Evaluating Forecasts – Forecast Metrics
• 19. Evaluating Forecasts – Validation Strategies