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

Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)

The first model that used some components from DL (we can’t call it DL because it is essentially a mix of DL and classical statistics) and made a splash in the field was a model that won the M4 competition (univariate) in 2018. This was a model by Slawek Smyl from Uber (at the time) and was a Frankenstein-style mix of exponential smoothing and an RNN, dubbed ES-RNN (Further reading has links to a newer and faster implementation of the model that uses GPU acceleration). This led to Makridakis et al. putting forward an argument that “hybrid approaches and combinations of methods are the way forward.” The creators of the N-BEATS model aspired to challenge this conclusion by designing a pure DL architecture for time series forecasting. They succeeded in this when they created a model that beat all other methods in the M4 competition (although they didn’t publish it in time to...

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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

Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)

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Authors (2)

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