book

Deep Learning for Time Series Cookbook

Name: Deep Learning for Time Series Cookbook
ISBN: 9781805129233

by Vitor Cerqueira, Luís Roque

March 2024

Beginner to intermediate

274 pages

5h 50m

English

Packt Publishing

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Deep Learning for Time Series Cookbook
ContributorsAbout the authorsAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedSectionsGetting readyHow to do it…How it works…There’s more…See alsoGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Chapter 1: Getting Started with Time Series
Technical requirementsLoading a time series using pandasGetting readyHow to do it…How it works…Visualizing a time seriesGetting readyHow to do it…How it works…There’s more…Resampling a time seriesGetting readyHow to do it…How it works…There’s more…Dealing with missing valuesGetting readyHow to do it…How it works…There’s more…Decomposing a time seriesGetting readyHow to do it…How it works…There’s more…See alsoComputing autocorrelationGetting readyHow to do it…How it works…There’s more…Detecting stationarityGetting readyHow to do it…How it works…There’s more…Dealing with heteroskedasticityGetting readyHow to do it…How it works…There’s more…See alsoLoading and visualizing a multivariate time seriesGetting readyHow to do it…How it works…Resampling a multivariate time seriesGetting readyHow to do it…How it works…Analyzing correlation among pairs of variablesGetting readyHow to do it…How it works…
Chapter 2: Getting Started with PyTorch
Technical requirementsInstalling PyTorchGetting readyHow to do it…How it works…Basic operations in PyTorchGetting readyHow to do it…How it works…Advanced operations in PyTorchGetting readyHow to do it…How it works…Building a simple neural network with PyTorchGetting readyHow to do it…There’s more…Training a feedforward neural networkGetting readyHow to do it…How it works…Training a recurrent neural networkGetting readyHow to do it…How it works…Training an LSTM neural networkGetting readyHow to do it…How it works…Training a convolutional neural networkGetting readyHow to do it…How it works…
Chapter 3: Univariate Time Series Forecasting
Technical requirementsBuilding simple forecasting modelsGetting readyHow to do it…How it works…There’s more…Univariate forecasting with ARIMAGetting readyHow to do it…How it works…There’s more…Preparing a time series for supervised learningGetting readyHow to do it…How it works…There’s more…Univariate forecasting with a feedforward neural networkGetting readyHow to do it…How it works…There’s more…Univariate forecasting with an LSTMGetting readyHow to do it…How it works…There’s more…Univariate forecasting with a GRUGetting readyHow to do it…How it works…There’s more…Univariate forecasting with a Stacked LSTMGetting readyHow to do it…How it works…Combining an LSTM with multiple fully connected layersGetting readyHow to do it…How it works…There’s more…Univariate forecasting with a CNNGetting readyHow to do it…How it works…There’s more…Handling trend – taking first differencesGetting readyHow to do it…How it works…There’s more…Handling seasonality – seasonal dummies and Fourier seriesGetting readyHow to do it…How it works…There’s more…Handling seasonality – seasonal differencingGetting readyHow to do it…How it works…Handling seasonality – seasonal decompositionGetting readyHow to do it…How it works…Handling non-constant variance – log transformationGetting readyHow to do it…How it works…
Chapter 4: Forecasting with PyTorch Lightning
Technical requirementsPreparing a multivariate time series for supervised learningGetting readyHow to do it…How it works…Training a linear regression model for forecasting with a multivariate time seriesGetting readyHow to do it…How it works…Feedforward neural networks for multivariate time series forecastingGetting readyHow to do it…How it works…There’s more…LSTM neural networks for multivariate time series forecastingGetting readyHow to do it…How it works…There’s more…Monitoring the training process using TensorboardGetting readyHow to do it…How it works…There’s more…Evaluating deep neural networks for forecastingGetting readyHow to do it…How it works…There’s more…Using callbacks – EarlyStoppingGetting readyHow to do it…How it works…There’s more…
Chapter 5: Global Forecasting Models
Technical requirementsMulti-step forecasting with multivariate time seriesGetting readyHow to do it…How it works…There’s more…Multi-step and multi-output forecasting with multivariate time seriesGetting readyHow to do it…How it works…Preparing multiple time series for a global modelGetting readyHow to do it…How it works…Training a global LSTM with multiple time seriesGetting readyHow to do it…How it works…Global forecasting models for seasonal time seriesGetting readyHow to do it…How it works…There’s more…Hyperparameter optimization using Ray TuneGetting readyHow to do it…How it works…There’s more…
Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting
Technical requirementsInterpretable forecasting with N-BEATSGetting readyHow to do it…How it works…There’s more…Optimizing the learning rate with PyTorch ForecastingGetting readyHow to do it…How it works…There’s more…Getting started with GluonTSGetting readyHow to do it…How it works…Training a DeepAR model with GluonTSGetting readyHow to do it…How it works…There’s more…Training a Transformer model with NeuralForecastGetting readyHow to do it…How it works…There’s more…Training a Temporal Fusion Transformer with GluonTSGetting readyHow to do it…How it works…There’s more…Training an Informer model with NeuralForecastGetting readyHow to do it…How it works…There’s more…Comparing different Transformers with NeuralForecastGetting readyHow to do it…How it works…
Chapter 7: Probabilistic Time Series Forecasting
Technical requirementsIntroduction to exceedance probability forecastingGetting readyHow to do it…How it works…There’s more…Exceedance probability forecasting with an LSTMGetting readyHow to do it…How it works…There’s more…Creating prediction intervals using conformal predictionGetting readyHow to do it…How it works…Probabilistic forecasting with an LSTMGetting readyHow to do it…How it works…Probabilistic forecasting with DeepARGetting readyHow to do it…How it works…Introduction to Gaussian ProcessesGetting readyHow to do it…How it works…Using Prophet for probabilistic forecastingGetting readyHow to do it…How it works…There’s more…
Chapter 8: Deep Learning for Time Series Classification
Technical requirementsTackling TSC with K-nearest neighborsGetting readyHow to do it…How it works…There’s more…Building a DataModule class for TSCGetting readyHow to do it…How it works…Convolutional neural networks for TSCGetting readyHow to do it…How it works…ResNets for TSCGetting readyHow to do it…How it works…Tackling TSC problems with sktimeGetting readyHow to do it…How it works…There’s more…

Chapter 9: Deep Learning for Time Series Anomaly Detection
Technical requirementsTime series anomaly detection with ARIMAGetting readyHow to do it…How it works…There’s more…Prediction-based anomaly detection using DLGetting readyHow to do it…How it works…There’s more…Anomaly detection using an LSTM AEGetting readyHow to do it…How it works…Building an AE using PyODGetting readyHow to do it…How it works…There’s more…Creating a VAE for time series anomaly detectionGetting readyHow to do it…How it works…There’s more…Using GANs for time series anomaly detectionGetting ready…How to do it…How it works…There’s more…
Index
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Overview

"Deep Learning for Time Series Cookbook" is a hands-on guide that delves into the application of deep learning techniques to time series data analysis. With practical Python and PyTorch recipes, you'll tackle real-world tasks such as forecasting future values, anomaly detection, and classification of temporal patterns. By the end of the book, you'll have the skills to develop production-ready models for time series challenges.

What this Book will help me do

Understand the foundations of time series analysis and its relation to deep learning.
Master PyTorch functionalities to build advanced neural network architectures for time series.
Develop models for tasks like forecasting, showcasing trends in temporal data.
Train deep learning models to detect anomalies in sequences, aiding diagnostics.
Implement classification techniques to categorize patterns in time series data effectively.

Author(s)

The authors, None Cerqueira and Luís Roque, bring years of expertise in data science and machine learning, with a special focus on time series and neural networks. They are seasoned practitioners and educators who excel at breaking down complex concepts into comprehensible recipes. Their approach emphasizes actionable knowledge, ensuring learners can apply what they read immediately.

Who is it for?

This book is ideal for data scientists, machine learning practitioners, and AI enthusiasts who are already familiar with Python programming and have a foundational understanding of machine learning. It's particularly geared towards professionals wanting to tackle time series problems with cutting-edge deep learning methods. Whether you are looking to forecast trends or classify temporal patterns, this book will elevate your skills tailored to these specific challenges.

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Time Series Analysis with Python Cookbook

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