You're reading from Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781835883181

Length 658 pages

Edition 2nd Edition

Languages

Python

Tools

PyTorch

Concepts

Data Science

Authors (2):

Jeffrey Tackes

Manu Joseph

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Table of Contents (26) Chapters

Preface

1. Part-1: Getting Familiar with Time Series FREE CHAPTER

2. Introducing Time Series

3. Acquiring and Processing Time Series Data

4. Analyzing and Visualizing Time Series Data

5. Setting a Strong Baseline Forecast

6. Part-2: Machine Learning for Time Series

7. Time Series Forecasting as Regression

8. Feature Engineering for Time Series Forecasting

9. Target Transformations for Time Series Forecasting

10. Forecasting Time Series with Machine Learning Models

11. Ensembling and Stacking

12. Global Forecasting Models

13. Part-3: Deep Learning for Time Series

14. Introduction to Deep Learning

15. Building Blocks of Deep Learning for Time Series

16. Common Modeling Patterns for Time Series

17. Attention and Transformers for Time Series

18. Strategies for Global Deep Learning Forecasting Models

19. Specialized Deep Learning Architectures for Forecasting

20. Probabilistic Forecasting and More

21. Part-4: Mechanics of Forecasting

22. Multi-Step Forecasting

23. Evaluating Forecast Errors—A Survey of Forecast Metrics

24. Evaluating Forecasts—Validation Strategies

25. Index

Direct strategy

The direct strategy, also called the independent strategy, is a popular strategy in forecasting that uses ML. This involves forecasting each horizon independently of each other. Let’s look at a diagram first:

Figure 18.3: Direct strategy for multi-step forecasting

Next, let’s discuss the regimes in detail.

Training regime

Under the direct strategy (Figure 18.3), we train H different models, which take in the same window function but are trained to predict different timesteps in the forecast horizon. Therefore, we learn a separate set of parameters, one for each timestep in the horizon, such that all the models combined learn a direct and independent mapping from the window, W(Y_t), to the forecast horizon, H.

This strategy has gained ground along with the popularity of ML-based time series forecasting. From the ML context, we can practically implement it in two ways:

Shifting targets: Each model in the horizon is trained...

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You're reading from Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Table of Contents (26) Chapters

Direct strategy

Training regime

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Authors (2)

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