About the Book:
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you will discover how to develop deep learning models for your own time series forecasting projects.
Table of Contents
Part 1: Foundations
Lesson 01: Promise of Deep Learning for Time Series Forecasting
Lesson 02: Taxonomy of Time Series Forecasting Problems
Lesson 03: How to Develop a Skillful Forecasting Model
Lesson 04: How to Transform Time Series to a Supervised Learning Problem
Lesson 05: How to Prepare Time Series Data for CNNs and LSTMs
Part 2: Deep Learning Modeling
Lesson 06: How to Prepare Time Series Data for CNNs and LSTMs
Lesson 07: How to Develop MLPs for Time Series Forecasting
Lesson 08: How to Develop CNNs for Time Series Forecasting
Lesson 09: How to Develop LSTMs for Time Series Forecasting
Part 3: Univariate Forecasting
Lesson 10: Review of Top Methods For Univariate Time Series Forecasting
Lesson 11: How to Develop Simple Methods for Univariate Forecasting
Lesson 12: How to Develop ETS Models for Univariate Forecasting
Lesson 13: How to Develop SARIMA Models for Univariate Forecasting
Lesson 14: How to Develop MLPs, CNNs and LSTMs for Univariate Forecasting
Lesson 15: How to Grid Search Deep Learning Models for Univariate Forecasting
Part 4: Multi-step Forecasting
Lesson 16: How to Load and Explore Household Energy Usage Data
Lesson 17: How to Develop Naive Models for Multi-step Energy Usage Forecasting
Lesson 18: How to Develop ARIMA Models for Multi-step Energy Usage Forecasting
Lesson 19: How to Develop CNNs for Multi-step Energy Usage Forecasting
Lesson 20: How to Develop LSTMs for Multi-step Energy Usage Forecasting
Part 5: Time Series Classification
Lesson 21: Review of Deep Learning Models for Human Activity Recognition
Lesson 22: How to Load and Explore Human Activity Data
Lesson 23: How to Develop ML Models for Human Activity Recognition
Lesson 24: How to Develop CNNs for Human Activity Recognition
Lesson 25: How to Develop LSTMs for Human Activity Recognition
