You're reading from Modern Time Series Forecasting with Python

Product type Book

Published in Nov 2022

Publisher Packt

ISBN-13 9781803246802

Pages 552 pages

Edition 1st Edition

Languages

Concepts

Data Science

Author (1):

Manu Joseph

Table of Contents (26) Chapters

Preface

1. Part 1 – Getting Familiar with Time Series

2. Chapter 1: Introducing Time Series

3. Chapter 2: Acquiring and Processing Time Series Data

4. Chapter 3: Analyzing and Visualizing Time Series Data

5. Chapter 4: Setting a Strong Baseline Forecast

6. Part 2 – Machine Learning for Time Series

7. Chapter 5: Time Series Forecasting as Regression

8. Chapter 6: Feature Engineering for Time Series Forecasting

9. Chapter 7: Target Transformations for Time Series Forecasting

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

11. Chapter 9: Ensembling and Stacking

12. Chapter 10: Global Forecasting Models

13. Part 3 – Deep Learning for Time Series

14. Chapter 11: Introduction to Deep Learning

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

16. Chapter 13: Common Modeling Patterns for Time Series

17. Chapter 14: Attention and Transformers for Time Series

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

19. Chapter 16: Specialized Deep Learning Architectures for Forecasting

20. Part 4 – Mechanics of Forecasting

21. Chapter 17: Multi-Step Forecasting

22. Chapter 18: Evaluating Forecasts – Forecast Metrics

23. Chapter 19: Evaluating Forecasts – Validation Strategies

24. Index

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Creating GFMs

Training a GFM is very straightforward. While we were training LFMs in Chapter 8, Forecasting Time Series with Machine Learning Models, we were looping over different households in the London Smart Meters dataset and training a model for each household. However, if we just take all the households into a single dataframe (our dataset is already that way) and train a single model on it, we get a GFM. One thing we want to keep in mind is to make sure that all the time series in the dataset have the same frequency. In other words, if we mix daily time series with weekly ones while training these models, the performance drop will be noticeable – especially if we are using time-varying features and other time-based information. For a purely autoregressive model, mixing time series in this way is much less of a problem.

Notebook alert

To follow along with the complete code, use the notebook named 01-Global Forecasting Models-ML.ipynb in the chapter10 folder.

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