Book Image

Deep Learning for Time Series Cookbook

By : Vitor Cerqueira, Luís Roque
Book Image

Deep Learning for Time Series Cookbook

By: Vitor Cerqueira, Luís Roque

Overview of this book

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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Sections
Get in touch
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1
Chapter 1: Getting Started with Time Series
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
2
Chapter 2: Getting Started with PyTorch
Chapter 2: Getting Started with PyTorch
Technical requirements
Installing PyTorch
Basic operations in PyTorch
Advanced operations in PyTorch
Building a simple neural network with PyTorch
Training a feedforward neural network
Training a recurrent neural network
Training an LSTM neural network
Training a convolutional neural network
3
Chapter 3: Univariate Time Series Forecasting
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
4
Chapter 4: Forecasting with PyTorch Lightning
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
5
Chapter 5: Global Forecasting Models
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
6
Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting
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
7
Chapter 7: Probabilistic Time Series Forecasting
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
8
Chapter 8: Deep Learning for Time Series Classification
Chapter 8: Deep Learning for Time Series Classification
Technical requirements
Tackling TSC with K-nearest neighbors
Building a DataModule class for TSC
Convolutional neural networks for TSC
ResNets for TSC
Tackling TSC problems with sktime
9
Chapter 9: Deep Learning for Time Series Anomaly Detection
Chapter 9: Deep Learning for Time Series Anomaly Detection
Technical requirements
Time series anomaly detection with ARIMA
Prediction-based anomaly detection using DL
Anomaly detection using an LSTM AE
Building an AE using PyOD
Creating a VAE for time series anomaly detection
Using GANs for time series anomaly detection
10
Index
Index
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11
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Univariate Time Series Forecasting

In this chapter, we’ll develop deep learning models to tackle univariate time series forecasting problems. We’ll touch on several aspects of time series preprocessing, such as preparing a time series for supervised learning and dealing with conditions such as trend or seasonality.

We’ll cover different types of models, including simple baselines such as the naïve or historical mean method. We’ll provide a brief background on a popular forecasting technique, autoregressive integrated moving average (ARIMA). Then, we’ll explain how to create a forecasting model using different types of deep learning methods. These include feedforward neural networks, long short-term memory (LSTM), gated recurrent units (GRU), Stacked LSTM, and convolutional neural networks (CNNs). You will also learn how to deal with common problems that arise in time series modeling; for example, how to deal with trend using first differences...

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