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 time-dependent 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 easy-to-follow 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 production-ready 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 non-constant 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
- Multi-step forecasting with multivariate time series
- Multi-step and multi-output 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 N-BEATS
- 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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