Preface

• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
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1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series FREE CHAPTER

• Technical requirements
• What is a time series?
• Data-generating process (DGP)
• What can we forecast?
• Forecasting terminology
• Summary
• Further reading

3. Chapter 2: Acquiring and Processing Time Series Data

• Technical requirements
• Understanding the time series dataset
• pandas datetime operations, indexing, and slicing 
– a refresher
• Handling missing data
• Mapping additional information
• Saving and loading files to disk
• Handling longer periods of missing data
• Summary

4. Chapter 3: Analyzing and Visualizing Time Series Data

• Technical requirements
• Components of a time series
• Visualizing time series data
• Decomposing a time series
• Detecting and treating outliers
• Summary
• References
• Further reading

5. Chapter 4: Setting a Strong Baseline Forecast

• Technical requirements
• Setting up a test harness
• Generating strong baseline forecasts
• Assessing the forecastability of a time series
• Summary
• References
• Further reading

6. Part 2 – Machine Learning for Time Series

7. Chapter 5: Time Series Forecasting as Regression

• Understanding the basics of machine learning
• Time series forecasting as regression
• Global forecasting models – a paradigm shift
• Summary
• References
• Further reading

8. Chapter 6: Feature Engineering for Time Series Forecasting

• Technical requirements
• Feature engineering
• Avoiding data leakage
• Setting a forecast horizon
• Time delay embedding
• Temporal embedding
• Summary

9. Chapter 7: Target Transformations for Time Series Forecasting

• Technical requirements
• Handling non-stationarity in time series
• Detecting and correcting for unit roots
• Detecting and correcting for trends
• Detecting and correcting for seasonality
• Detecting and correcting for heteroscedasticity
• AutoML approach to target transformation
• Summary
• References
• Further reading

10. Chapter 8: Forecasting Time Series with Machine Learning Models

• Technical requirements
• Training and predicting with machine learning models
• Generating single-step forecast baselines
• Standardized code to train and evaluate machine learning models
• Training and predicting for multiple households
• Summary
• References
• Further reading

11. Chapter 9: Ensembling and Stacking

• Technical requirements
• Combining forecasts
• Stacking or blending
• Summary
• References
• Further reading

12. Chapter 10: Global Forecasting Models

• Technical requirements
• Why Global Forecasting Models (GFMs)?
• Creating GFMs
• Strategies to improve GFMs
• Bonus – interpretability
• Summary
• References
• Further reading

13. Part 3 – Deep Learning for Time Series

14. Chapter 11: Introduction to Deep Learning

• Technical requirements
• What is deep learning and why now?
• Components of a deep learning system
• Summary
• References
• Further reading

15. Chapter 12: Building Blocks of Deep Learning for Time Series

• Technical requirements
• Understanding the encoder-decoder paradigm
• Feed-forward networks
• Recurrent neural networks
• Long short-term memory (LSTM) networks
• Gated recurrent unit (GRU)
• Convolution networks
• Summary
• References
• Further reading

16. Chapter 13: Common Modeling Patterns for Time Series

• Technical requirements
• Tabular regression
• Single-step-ahead recurrent neural networks
• Sequence-to-sequence (Seq2Seq) models
• Summary
• Reference
• Further reading

17. Chapter 14: Attention and Transformers for Time Series

• Technical requirements
• What is attention?
• The generalized attention model
• Forecasting with sequence-to-sequence models and attention
• Transformers – Attention is all you need
• Forecasting with Transformers
• Summary
• References
• Further reading

18. Chapter 15: Strategies for Global Deep Learning Forecasting Models

• Technical requirements
• Creating global deep learning forecasting models
• Using time-varying information
• Using static/meta information
• Using the scale of the time series
• Balancing the sampling procedure
• Summary
• Further reading

19. Chapter 16: Specialized Deep Learning Architectures for Forecasting

• Technical requirements
• The need for specialized architectures
• Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)
• Neural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (N-BEATSx)
• Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)
• Informer
• Autoformer
• Temporal Fusion Transformer (TFT)
• Interpretability
• Probabilistic forecasting
• Summary
• References
• Further reading

20. Part 4 – Mechanics of Forecasting

21. Chapter 17: Multi-Step Forecasting

• Why multi-step forecasting?
• Recursive strategy
• Direct strategy
• Joint strategy
• Hybrid strategies
• How to choose a multi-step forecasting strategy?
• Summary
• References

22. Chapter 18: Evaluating Forecasts – Forecast Metrics

• Technical requirements
• Taxonomy of forecast error measures
• Investigating the error measures
• Experimental study of the error measures
• Guidelines for choosing a metric
• Summary
• References
• Further reading

23. Chapter 19: Evaluating Forecasts – Validation Strategies

• Technical requirements
• Model validation
• Holdout strategies
• Cross-validation strategies
• Choosing a validation strategy
• Validation strategies for datasets with multiple time series
• Summary
• References
• Further reading

24. Index

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