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

Data-generating process (DGP)

We saw that time series data is a collection of observations made sequentially along the time dimension. Any time series is, in turn, generated by some kind of mechanism. For example, time series data of daily shipments of your favorite chocolate from the manufacturing plant is affected by a lot of factors such as the time of the year, the holiday season, the availability of cocoa, the uptime of the machines working on the plant, and so on. In statistics, this underlying process that generated the time series is referred to as the DGP. Time series data is produced by stochastic and deterministic process. The deterministic processes involve quantities that evolve in a predictable manner over time. An example of this is the radioactive decay of an element, where the remaining quantity diminishes according to a precise mathematical formula, leading to a consistent reduction over time. But most of the interesting time series (from a forecasting perspective) are...

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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

Data-generating process (DGP)

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Authors (2)

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