Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection - Softcover

Inhaltsangabe

Build real-world Artificial Intelligence applications with Time Series data and Deep Learning

Take your deep learning skills to the next level by mastering PyTorch with tens of Python recipes

Solve forecasting problems and predict the future using advanced neural network architectures in PyTorch

Key Features

• Learn how to train accurate forecasting model using neural networks and real-world time series
• Build advanced deep neural network architectures using PyTorch
• Tackle several time series tasks, such as forecasting, classification, hierarchical forecasting, and anomaly detection

Book Description

Many real-world systems are captured through the lens of time series. The analysis and forecasting of time series has thus become a key aspect of several organizations. Deep learning is the hottest Artificial Intelligence technology. It leverages large amounts of data to build intricate and accurate forecasting models.

This book is a comprehensive cookbook that guides you through the development of deep learning models for time series data using PyTorch. We start from the basic concepts behind time series analysis and the PyTorch framework. Then, we dive into the details of several time series problems, including forecasting, classification, anomaly detection, and hierarchical time series forecasting. You'll learn how to tackle these tasks with a set of code recipes.

By the end of this book, you'll have a solid understanding of time series data problems and how to tackle them using deep learning based on PyTorch.

What you will learn

• Understand main time series analysis concepts and how to apply them using pandas
• Learn about PyTorch and how to use it to build deep learning models
• Explore how to transform a time series for training transformers and other advanced deep neural networks
• Understand how to deal with various time series characteristics, such as trend, seasonality, or non-constant variance
• Tackle different kinds of forecasting problems, involving univariate, multivariate, or hierarchical time series
• Understand how to apply residual and convolutional neural networks for time series classification problems
• Learn how to solve time series anomaly detection problems using auto-encoders and Generative Adversarial Networks

Who This Book Is For

If you are a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. In order to learn from this book, you should have basic knowledge of Python and machine learning.

Table of Contents

1. Getting Started with Time Series
2. Getting Started with keras
3. Univariate Time Series Forecasting
4. Advanced Forecasting Problems
5. Advanced Deep Learning Architectures for Time Series Forecasting
6. Probabilistic Time Series Forecasting
7. Deep Learning for Time Series Classification
8. Deep Learning for Time Series Anomaly Detection

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