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

Technical requirements

You will need to set up the Anaconda environment following the instructions in the Preface of the book to get a working environment with all the libraries and datasets required for the code in this book. Any additional library will be installed while running the notebooks.

You will need to run the following notebooks before using the code in this chapter:

02-Preprocessing_London_Smart_Meter_Dataset.ipynb in Chapter02
01-Setting_up_Experiment_Harness.ipynb in Chapter04
From the Chapter06 and Chapter07 folders:
- 01-Feature_Engineering.ipynb
- 02-Dealing_with_Non-Stationarity.ipynb
- 02a-Dealing_with_Non-Stationarity-Train+Val.ipynb
From the Chapter08 folder:
- 00-Single_Step_Backtesting_Baselines.ipynb
- 01-Forecasting_with_ML.ipynb
- 01a-Forecasting_with_ML_for_Test_Dataset.ipynb
- 02-Forecasting_with_Target_Transformation.ipynb
- 02a-Forecasting_with_Target_Transformation...

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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

Technical requirements

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Authors (2)

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