Modern Time Series Forecasting with Python

Modern Time Series Forecasting with Python

Contents

Preface

Part 1: Getting Familiar with Time Series

Chapter 1: Introducing Time Series Chapter 2: Acquiring and Processing Time Series Data Chapter 3: Analyzing and Visualizing Time Series Data Chapter 4: Setting a Strong Baseline Forecast

Part 2: Machine Learning for Time Series

Chapter 5: Time Series Forecasting as Regression Chapter 6: Feature Engineering for Time Series Forecasting Chapter 7: Target Transformations for Time Series Forecasting Chapter 8: Forecasting Time Series with Machine Learning Models Chapter 9: Ensembling and Stacking Chapter 10: Global Forecasting Models

Part 3: Deep Learning for Time Series

Chapter 11: Introduction to Deep Learning Chapter 12: Building Blocks of Deep Learning for Time Series Chapter 13: Common Modeling Patterns for Time Series Chapter 14: Attention and Transformers for Time Series Chapter 15: Strategies for Global Deep Learning Forecasting Models Chapter 16: Specialized Deep Learning Architectures for Forecasting Chapter 17: Probabilistic Forecasting and More

Part 4: Forecasting Mechanics

Chapter 18: Multi-Step Forecasting Chapter 19: Evaluating Forecast Errors—A Survey of Forecast Metrics Chapter 20: Evaluating Forecasts—Validation Strategies