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

What can we forecast?

Before we move ahead, there is another aspect of time series forecasting that we have to understand—the predictability of a time series. The most basic assumption when we forecast a time series is that the future depends on the past. But not all time series are equally predictable.

Let's take a look at a few examples and try to rank these in order of predictability (from easiest to hardest), as follows:

High tide next Monday
Lottery numbers next Sunday
The stock price of Tesla next Friday

Intuitively, it is very easy for us to rank them. High tide next Monday is going to be the easiest to predict because it is so predictable, the lottery numbers are going to be very hard to predict because these are pretty much random, and the stock price of Tesla next Friday is going to be difficult to predict, but not impossible.

NOTE

However, for people thinking that they can forecast stock prices with the advanced techniques covered in the book and get rich, that...

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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

What can we forecast?

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Authors (2)

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