Deep Learning for Time Series Cookbook

By : Vitor Cerqueira, Luís Roque

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

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

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

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

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

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

Index

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Analyzing correlation among pairs of variables

This recipe walks you through the process of using correlation to analyze a multivariate time series. This task is useful to understand the relationship among the different variables in the series and thereby understand its dynamics.

Getting ready

A common way to analyze the dynamics of multiple variables is by computing the correlation of each pair. You can use this information to perform feature selection. For example, when pairs of variables are highly correlated, you may want to keep only one of them.

How to do it…

First, we compute the correlation among each pair of variables:

corr_matrix = data_daily.corr(method='pearson')

We can visualize the results using a heatmap from the seaborn library:

import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(data=corr_matrix,
            cmap=sns.diverging_palette(230, 20, as_cmap=True),
            xticklabels=data_daily.columns,
            yticklabels=data_daily.columns,
            center=0,
            square=True,
            linewidths=.5,
            cbar_kws={"shrink": .5})
plt.xticks(rotation=30)

Heatmaps are a common way of visualizing matrices. We pick a diverging color set from sns.diverging_palette to distinguish between negative correlation (blue) and positive correlation (red).

How it works…

The following figure shows the heatmap with the correlation results:

Figure 1.7: Correlation matrix for a multivariate time series

The corr() method computes the correlation among each pair of variables in the data_daily object. In this case, we use the Pearson correlation with the method='pearson' argument. Kendall and Spearman are two common alternatives to the Pearson correlation.

Deep Learning for Time Series Cookbook

By : Vitor Cerqueira, Luís Roque

Deep Learning for Time Series Cookbook

By: Vitor Cerqueira, Luís Roque

Overview of this book

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Analyzing correlation among pairs of variables

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