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Deep Learning for Time Series Data Cookbook

✍ Scribed by Vitor Cerqueira, Luís Roque

Publisher
Packt Publishing
Year
2024
Tongue
English
Leaves
274
Category
Library
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No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with Time Series
Technical requirements
Loading a time series using pandas
Getting ready
How to do it…
How it works…
Visualizing a time series
Getting ready
How to do it…
How it works…
There’s more…
Resampling a time series
Getting ready
How to do it…
How it works…
There’s more…
Dealing with missing values
Getting ready
How to do it…
How it works…
There’s more…
Decomposing a time series
Getting ready
How to do it…
How it works…
There’s more…
See also
Computing autocorrelation
Getting ready
How to do it…
How it works…
There’s more…
Detecting stationarity
Getting ready
How to do it…
How it works…
There’s more…
Dealing with heteroskedasticity
Getting ready
How to do it…
How it works…
There’s more…
See also
Loading and visualizing a multivariate time series
Getting ready
How to do it…
How it works…
Resampling a multivariate time series
Getting ready
How to do it…
How it works…
Analyzing correlation among pairs of variables
Getting ready
How to do it…
How it works…
Chapter 2: Getting Started with PyTorch
Technical requirements
Installing PyTorch
Getting ready
How to do it…
How it works…
Basic operations in PyTorch
Getting ready
How to do it…
How it works…
Advanced operations in PyTorch
Getting ready
How to do it…
How it works…
Building a simple neural network with PyTorch
Getting ready
How to do it…
There’s more…
Training a feedforward neural network
Getting ready
How to do it…
How it works…
Training a recurrent neural network
Getting ready
How to do it…
How it works…
Training an LSTM neural network
Getting ready
How to do it…
How it works…
Training a convolutional neural network
Getting ready
How to do it…
How it works…
Chapter 3: Univariate Time Series Forecasting
Technical requirements
Building simple forecasting models
Getting ready
How to do it…
How it works…
There’s more…
Univariate forecasting with ARIMA
Getting ready
How to do it…
How it works…
There’s more…
Preparing a time series for supervised learning
Getting ready
How to do it…
How it works…
There’s more…
Univariate forecasting with a feedforward neural network
Getting ready
How to do it…
How it works…
There’s more…
Univariate forecasting with an LSTM
Getting ready
How to do it…
How it works…
There’s more…
Univariate forecasting with a GRU
Getting ready
How to do it…
How it works…
There’s more…
Univariate forecasting with a Stacked LSTM
Getting ready
How to do it…
How it works…
Combining an LSTM with multiple fully connected layers
Getting ready
How to do it…
How it works…
There’s more…
Univariate forecasting with a CNN
Getting ready
How to do it…
How it works…
There’s more…
Handling trend – taking first differences
Getting ready
How to do it…
How it works…
There’s more…
Handling seasonality – seasonal dummies and Fourier series
Getting ready
How to do it…
How it works…
There’s more…
Handling seasonality – seasonal differencing
Getting ready
How to do it…
How it works…
Handling seasonality – seasonal decomposition
Getting ready
How to do it…
How it works…
Handling non-constant variance – log transformation
Getting ready
How to do it…
How it works…
Chapter 4: Forecasting with PyTorch Lightning
Technical requirements
Preparing a multivariate time series for supervised learning
Getting ready
How to do it…
How it works…
Training a linear regression model for forecasting with a multivariate time series
Getting ready
How to do it…
How it works…
Feedforward neural networks for multivariate time series forecasting
Getting ready
How to do it…
How it works…
There’s more…
LSTM neural networks for multivariate time series forecasting
Getting ready
How to do it…
How it works…
There’s more…
Monitoring the training process using Tensorboard
Getting ready
How to do it…
How it works…
There’s more…
Evaluating deep neural networks for forecasting
Getting ready
How to do it…
How it works…
There’s more…
Using callbacks – EarlyStopping
Getting ready
How to do it…
How it works…
There’s more…
Chapter 5: Global Forecasting Models
Technical requirements
Multi-step forecasting with multivariate time series
Getting ready
How to do it…
How it works…
There’s more…
Multi-step and multi-output forecasting with multivariate time series
Getting ready
How to do it…
How it works…
Preparing multiple time series for a global model
Getting ready
How to do it…
How it works…
Training a global LSTM with multiple time series
Getting ready
How to do it…
How it works…
Global forecasting models for seasonal time series
Getting ready
How to do it…
How it works…
There’s more…
Hyperparameter optimization using Ray Tune
Getting ready
How to do it…
How it works…
There’s more…
Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting
Technical requirements
Interpretable forecasting with N-BEATS
Getting ready
How to do it…
How it works…
There’s more…
Optimizing the learning rate with PyTorch Forecasting
Getting ready
How to do it…
How it works…
There’s more…
Getting started with GluonTS
Getting ready
How to do it…
How it works…
Training a DeepAR model with GluonTS
Getting ready
How to do it…
How it works…
There’s more…
Training a Transformer model with NeuralForecast
Getting ready
How to do it…
How it works…
There’s more…
Training a Temporal Fusion Transformer with GluonTS
Getting ready
How to do it…
How it works…
There’s more…
Training an Informer model with NeuralForecast
Getting ready
How to do it…
How it works…
There’s more…
Comparing different Transformers with NeuralForecast
Getting ready
How to do it…
How it works…
Chapter 7: Probabilistic Time Series Forecasting
Technical requirements
Introduction to exceedance probability forecasting
Getting ready
How to do it…
How it works…
There’s more…
Exceedance probability forecasting with an LSTM
Getting ready
How to do it…
How it works…
There’s more…
Creating prediction intervals using conformal prediction
Getting ready
How to do it…
How it works…
Probabilistic forecasting with an LSTM
Getting ready
How to do it…
How it works…
Probabilistic forecasting with DeepAR
Getting ready
How to do it…
How it works…
Introduction to Gaussian Processes
Getting ready
How to do it…
How it works…
Using Prophet for probabilistic forecasting
Getting ready
How to do it…
How it works…
There’s more…
Chapter 8: Deep Learning for Time Series Classification
Technical requirements
Tackling TSC with K-nearest neighbors
Getting ready
How to do it…
How it works…
There’s more…
Building a DataModule class for TSC
Getting ready
How to do it…
How it works…
Convolutional neural networks for TSC
Getting ready
How to do it…
How it works…
ResNets for TSC
Getting ready
How to do it…
How it works…
Tackling TSC problems with sktime
Getting ready
How to do it…
How it works…
There’s more…
Chapter 9: Deep Learning for Time Series Anomaly Detection
Technical requirements
Time series anomaly detection with ARIMA
Getting ready
How to do it…
How it works…
There’s more…
Prediction-based anomaly detection using DL
Getting ready
How to do it…
How it works…
There’s more…
Anomaly detection using an LSTM AE
Getting ready
How to do it…
How it works…
Building an AE using PyOD
Getting ready
How to do it…
How it works…
There’s more…
Creating a VAE for time series anomaly detection
Getting ready
How to do it…
How it works…
There’s more…
Using GANs for time series anomaly detection
Getting ready…
How to do it…
How it works…
There’s more…
Index
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