You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781801075541

Length 630 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Data Analysis

Author (1):

Tarek A. Atwan

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Table of Contents (18) Chapters

Preface

1. Chapter 1: Getting Started with Time Series Analysis

2. Chapter 2: Reading Time Series Data from Files FREE CHAPTER

3. Chapter 3: Reading Time Series Data from Databases

4. Chapter 4: Persisting Time Series Data to Files

5. Chapter 5: Persisting Time Series Data to Databases

6. Chapter 6: Working with Date and Time in Python

7. Chapter 7: Handling Missing Data

8. Chapter 8: Outlier Detection Using Statistical Methods

9. Chapter 9: Exploratory Data Analysis and Diagnosis

10. Chapter 10: Building Univariate Time Series Models Using Statistical Methods

11. Chapter 11: Additional Statistical Modeling Techniques for Time Series

12. Chapter 12: Forecasting Using Supervised Machine Learning

13. Chapter 13: Deep Learning for Time Series Forecasting

14. Chapter 14: Outlier Detection Using Unsupervised Machine Learning

15. Chapter 15: Advanced Techniques for Complex Time Series

16. Index

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Forecasting with an RNN using PyTorch

In the previous recipes, you used Keras to build different deep learning architectures with minimal changes to code. This is one of the advantages of a high-level API – it allows you to explore and experiment with different architectures very easily.

In this recipe, you will build a simple RNN architecture using PyTorch, a low-level API.

Getting ready

You will be using the functions and steps used to prepare the time series for supervised learning. The one exception is with the features_target_ts function, it will be modified to return a PyTorch Tensor object as opposed to a NumPy ndarray object. In PyTorch, tensor is a data structure similar to NumPy's ndarray object but optimized to work with Graphical Processing Units (GPUs).

You can convert a NumPy ndarray to a PyTorch Tensor object using the torch.from_numpy() method and convert a PyTorch Tensor object to a NumPy ndarray object using the detach.numpy() method:

numpy_array...

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You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Table of Contents (18) Chapters

Forecasting with an RNN using PyTorch

Getting ready

Authors (1)

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You're reading from Time Series Analysis with Python Cookbook Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Table of Contents (18) Chapters

Forecasting with an RNN using PyTorch

Getting ready

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Authors (1)

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