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Deep Learning for Time Series Cookbook

You're reading from  Deep Learning for Time Series Cookbook

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781805129233
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Vitor Cerqueira Vitor Cerqueira
Profile icon Vitor Cerqueira
Luís Roque Luís Roque
Profile icon Luís Roque
View More author details

Table of Contents (12) Chapters

Preface Chapter 1: Getting Started with Time Series Chapter 2: Getting Started with PyTorch Chapter 3: Univariate Time Series Forecasting Chapter 4: Forecasting with PyTorch Lightning Chapter 5: Global Forecasting Models Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting Chapter 7: Probabilistic Time Series Forecasting Chapter 8: Deep Learning for Time Series Classification Chapter 9: Deep Learning for Time Series Anomaly Detection Index Other Books You May Enjoy

Preparing multiple time series for a global model

Now, it is time to move on to the type of time series problems that involve multiple time series. In this recipe, we will learn the fundamentals of global forecasting models and how they work. We’ll also explore how to prepare a dataset that contains multiple time series for forecasting. Again, we leverage the capabilities of the TimeSeriesDataSet and DataModule classes to help us do this.

Getting ready

So far, we’ve been working with time series problems involving a single dataset. Now, we’ll learn about global forecasting models, including the following:

  • Transitioning from local to global models: Initially, our work with time series forecasting focused on single datasets, where models predict future values based on historical data of one series. These so-called local models are tailored to specific time series, whereas global models involve handling multiple related time series and capturing relevant...
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