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

Feed-forward networks

Feed-forward networks (FFNs) or fully connected networks are the most basic architecture a neural network can take. We discussed perceptrons in Chapter 11, Introduction to Deep Learning. If we stack multiple perceptrons (both linear units and non-linear activations) and create a network of such units, we get what we call an FFN. The following diagram will help us understand this:

Figure 12.2: A Feed Forward Network (FFN)

An FFN takes a fixed-size input vector and passes it through a series of computational layers leading up to the desired output. This architecture is called feed-forward because the information is fed forward through the network. This is also called a fully connected network because every unit in a layer is connected to every unit in the previous layer and every unit in the next layer.

The first layer is called the input layer, and this is equal to the dimension of the input. The last layer is called the output layer, which is...

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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

Feed-forward networks

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Authors (2)

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