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

View More author details

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

Forecasting terminology

There are a few terminologies that will help you follow the book as well as other literature on time series. These are described in more detail here:

Forecasting

Forecasting is the prediction of future values of a time series using the known past values of the time series and/or some other related variables. This is very similar to prediction in ML where we use a model to predict unseen data.

Multivariate forecasting

Multivariate time series consist of more than one time series variable that is not only dependent on its past values but also has some dependency on the other variables. For example, a set of macroeconomic indicators such as gross domestic product (GDP), inflation, and so on of a particular country can be considered as a multivariate time series. The aim of multivariate forecasting is to come up with a model that captures the interrelationship between the different variables along with its relationship with its past and forecast all the time series...

The rest of the chapter is locked

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

Forecasting terminology

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you