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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25. Other Books You May Enjoy

Balancing the sampling procedure

We saw a few strategies for improving a global deep learning model by adding new types of features. Now, let’s look at a different aspect that is relevant in a global modeling context. In an earlier section, when we were talking about global deep learning models, we talked about how the process by which we sample a window of sequence to feed to our model can be thought of as a two-step process:

Sampling a time series out of a set of time series
Sampling a window out of that time series

Let’s use an analogy to make the concept clearer. Imagine we have a large bowl that we have filled with balls. Each ball in the bowl represents a time series in the dataset (a household in our dataset). Now, each ball, , has chits of paper representing all the different windows of samples we can draw from it.