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

Introduction to NeuralForecast

NeuralForecast is yet another library from the wonderful folks at NIXTLA. You might recall the name from Chapter 4, Setting a Strong Baseline Forecast, where we used statsforecast for classical time series models like ARIMA, ETS, and so on. They have a whole suite of open-source libraries for time series forecasting (mlforecast for machine learning based forecasts, hierarchicalforecast for reconciling forecasts for hierarchical data, utilsforecast with some utilities for forecasting, datasetsforecast with some ready-to-use datasets, and TimeGPT, their foundational model for time series).

Since we have learned how to use statsforecast, extending that to neuralforecast is going to be easy because both libraries maintain similar APIs, structure, and ways of working. neuralforecast offers both classic and cutting-edge deep learning models in an easy-to-use API, which makes it perfect for the practical side of the chapter.

NeuralForecast is structured...

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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

Introduction to NeuralForecast

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Authors (2)

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