Textbook in PDF format

Preface
Introduction
Foundations
Promise of Deep Learning for Time Series Forecasting
Time Series Forecasting
Convolutional Neural Networks for Time Series
Recurrent Neural Networks for Time Series
Promise of Deep Learning
Extensions
Further Reading
Summary
Taxonomy of Time Series Forecasting Problems
Framework Overview
Inputs vs. Outputs
Endogenous vs. Exogenous
Regression vs. Classification
Unstructured vs. Structured
Univariate vs. Multivariate
Single-step vs. Multi-step
Static vs. Dynamic
Contiguous vs. Discontiguous
Framework Review
Extensions
Further Reading
Summary
How to Develop a Skillful Forecasting Model
The Situation
Process Overview
How to Use This Process
Step 1: Define Problem
Step 2: Design Test Harness
Step 3: Test Models
Step 4: Finalize Model
Extensions
Further Reading
Summary
How to Transform Time Series to a Supervised Learning Problem
Supervised Machine Learning
Sliding Window
Sliding Window With Multiple Variates
Sliding Window With Multiple Steps
Implementing Data Preparation
Extensions
Further Reading
Summary
Review of Simple and Classical Forecasting Methods
Simple Forecasting Methods
Autoregressive Methods
Exponential Smoothing Methods
Extensions
Further Reading
Summary
Deep Learning Methods
How to Prepare Time Series Data for CNNs and LSTMs
Overview
Time Series to Supervised
D Data Preparation Basics
Data Preparation Example
Extensions
Further Reading
Summary
How to Develop MLPs for Time Series Forecasting
Tutorial Overview
Univariate MLP Models
Multivariate MLP Models
Multi-step MLP Models
Multivariate Multi-step MLP Models
Extensions
Further Reading
Summary
..................................................
..................................................

Overview
Download Anaconda
Install Anaconda
Start and Update Anaconda
Install Deep Learning Libraries
Further Reading
Summary
Conclusions
How Far You Have Come