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

Assessing the forecastability of a time series

Although there are many statistical measures that we can use to assess the predictability of a time series, we will just look at a few that are easier to understand and practical when dealing with large time series datasets. The associated notebook (02-Forecastability.ipynb) contains the code to follow along.

Coefficient of Variation (CoV)

The Coefficient of Variation (CoV) relies on the intuition that the more variability that you find in a time series, the harder it is to predict it. And how do we measure variability in a random variable? Standard deviation.

In many real-world time series, the variation we see in the time series is dependent on the scale of the time series. Let's imagine that there are two retail products, A and B. A has a mean monthly sale of 15, while B has 50. If we look at a few real-world examples like this, we will see that if A and B have the same standard deviation, B, which has a higher mean, is much more...

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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

Assessing the forecastability of a time series

Coefficient of Variation (CoV)

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Authors (2)

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