Book description
Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes
Key Features
 Learn the fundamentals of time series analysis and how to model time series data using deep learning
 Explore the world of deep learning with PyTorch and build advanced deep neural networks
 Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection
 Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Most organizations exhibit a timedependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.
This book guides you through applying deep learning to time series data with the help of easytofollow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build productionready prediction solutions.
By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.
What you will learn
 Grasp the core of time series analysis and unleash its power using Python
 Understand PyTorch and how to use it to build deep learning models
 Discover how to transform a time series for training transformers
 Understand how to deal with various time series characteristics
 Tackle forecasting problems, involving univariate or multivariate data
 Master time series classification with residual and convolutional neural networks
 Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)
Who this book is for
If you’re a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.
Table of contents
 Deep Learning for Time Series Cookbook
 Contributors
 About the authors
 About the reviewer
 Preface

Chapter 1: Getting Started with Time Series
 Technical requirements
 Loading a time series using pandas
 Visualizing a time series
 Resampling a time series
 Dealing with missing values
 Decomposing a time series
 Computing autocorrelation
 Detecting stationarity
 Dealing with heteroskedasticity
 Loading and visualizing a multivariate time series
 Resampling a multivariate time series
 Analyzing correlation among pairs of variables
 Chapter 2: Getting Started with PyTorch

Chapter 3: Univariate Time Series Forecasting
 Technical requirements
 Building simple forecasting models
 Univariate forecasting with ARIMA
 Preparing a time series for supervised learning
 Univariate forecasting with a feedforward neural network
 Univariate forecasting with an LSTM
 Univariate forecasting with a GRU
 Univariate forecasting with a Stacked LSTM
 Combining an LSTM with multiple fully connected layers
 Univariate forecasting with a CNN
 Handling trend – taking first differences
 Handling seasonality – seasonal dummies and Fourier series
 Handling seasonality – seasonal differencing
 Handling seasonality – seasonal decomposition
 Handling nonconstant variance – log transformation

Chapter 4: Forecasting with PyTorch Lightning
 Technical requirements
 Preparing a multivariate time series for supervised learning
 Training a linear regression model for forecasting with a multivariate time series
 Feedforward neural networks for multivariate time series forecasting
 LSTM neural networks for multivariate time series forecasting
 Monitoring the training process using Tensorboard
 Evaluating deep neural networks for forecasting
 Using callbacks – EarlyStopping

Chapter 5: Global Forecasting Models
 Technical requirements
 Multistep forecasting with multivariate time series
 Multistep and multioutput forecasting with multivariate time series
 Preparing multiple time series for a global model
 Training a global LSTM with multiple time series
 Global forecasting models for seasonal time series
 Hyperparameter optimization using Ray Tune

Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting
 Technical requirements
 Interpretable forecasting with NBEATS
 Optimizing the learning rate with PyTorch Forecasting
 Getting started with GluonTS
 Training a DeepAR model with GluonTS
 Training a Transformer model with NeuralForecast
 Training a Temporal Fusion Transformer with GluonTS
 Training an Informer model with NeuralForecast
 Comparing different Transformers with NeuralForecast

Chapter 7: Probabilistic Time Series Forecasting
 Technical requirements
 Introduction to exceedance probability forecasting
 Exceedance probability forecasting with an LSTM
 Creating prediction intervals using conformal prediction
 Probabilistic forecasting with an LSTM
 Probabilistic forecasting with DeepAR
 Introduction to Gaussian Processes
 Using Prophet for probabilistic forecasting
 Chapter 8: Deep Learning for Time Series Classification
 Chapter 9: Deep Learning for Time Series Anomaly Detection
 Index
 Other Books You May Enjoy
Product information
 Title: Deep Learning for Time Series Cookbook
 Author(s):
 Release date: March 2024
 Publisher(s): Packt Publishing
 ISBN: 9781805129233
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