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

Convolution networks

Convolution networks, also called convolutional neural networks (CNNs), are like neural networks for processing data in the form of a grid. This grid can be 2D (such as an image), 1D (such as a time series), 3D (such as data from LIDAR sensors), and so on. Although this book is about time series and, typically, 1D convolutions are used in time series forecasting, it’s easier to understand convolutions in the 2D context (an image and so on) and then move back to a single-dimensional grid for time series.

The basic idea behind CNNs is inspired by how human vision works. In 1979, Fukushima proposed Neocognitron (Reference 12). It was a one-of-a-kind architecture that was directly inspired by how human vision works. But CNNs came into existence as we know them today in 1989 when Yann Le Cun used backpropagation to learn such a network, proving it by getting state-of-the-art results in handwritten digit recognition (Reference 13). In 2012, when AlexNet ...

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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

Convolution networks

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