0215arthur / deep-learning-time-series-forecasting

Deep Learning for Time Series Forecasting

Overview

Predict the Future with MLPs, CNNs and LSTMs in Python

Description

Welcome to Deep Learning for Time Series Forecasting. Deep learning methods, such as Multilayer Perceptrons, Convolutional Neural Networks, and Long Short-Term Memory Networks, can be used to automatically learn the temporal dependence structures for challenging time series forecasting problems. Neural networks may not be the best solution for all time series forecasting problems, but for those problems where classical methods fail and machine learning methods require elaborate feature engineering, deep learning methods can be used with great success. This book was designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.

Labs

Labs for this course are available at path shared below. Elev8ed Notebooks (powered by Jupyter) will be accessible at the port given to you by your instructor.

1. How to Prepare Time Series Data for CNNs and LSTMs

    * <host-ip>:<port>/lab/workspaces/lab1_Prepare_Time_Series_For_Data_CNNs_and_LSTMs
   

2. How to Develop MLPs for Time Series Forecasting (Part 1)

    * <host-ip>:<port>/lab/workspaces/lab2_MLPs_Time_Series_Forecasting_Part1
   

3. How to Develop MLPs for Time Series Forecasting (Part 2)

    * <host-ip>:<port>/lab/workspaces/lab3_MLPs_Time_Series_Forecasting_Part2
   

4. How to Develop CNNs for Time Series Forecasting (Part 1)

    * <host-ip>:<port>/lab/workspaces/lab4_CNNs_Time_Series_Forecasting_Part1
   

5. How to Develop CNNs for Time Series Forecasting (Part 2)

    * <host-ip>:<port>/lab/workspaces/lab5_CNNs_Time_Series_Forecasting_Part2
   

6. How to Develop LSTMs for Time Series Forecasting (Part 1)

    * <host-ip>:<port>/lab/workspaces/lab6_LSTMs_Time_Series_Forecasting_Part1
   

7. How to Develop LSTMs for Time Series Forecasting (Part 2)

    * <host-ip>:<port>/lab/workspaces/lab7_LSTMs_Time_Series_Forecasting_Part2
   

8. How to Develop Simple Methods for Univariate Forecasting (Part 1)

    * <host-ip>:<port>/lab/workspaces/lab8_Simple_Methods_Univariate_Forecasting_Part1
   

9. How to Develop Simple Methods for Univariate Forecasting (Part 2)

    * <host-ip>:<port>/lab/workspaces/lab9_Simple_Methods_Univariate_Forecasting_Part2
   

10. How to Develop ETS Models for Univariate Forecasting

    * <host-ip>:<port>/lab/workspaces/lab10_ETS_Models_Univariate_Forecasting
    

11. How to Develop SARIMA Models for Univariate Forecasting

    * <host-ip>:<port>/lab/workspaces/lab11_SARIMA_Models_Univariate_Forecasting
    

12. How to Develop MLPs, CNNs and LSTMs for Univariate Forecasting

    * <host-ip>:<port>/lab/workspaces/lab12_MLPs_CNNs_and_LSTMs_Univariate_Forecasting
    

13. How to Grid Search Deep Learning Models for Univariate Forecasting

    * <host-ip>:<port>/lab/workspaces/lab13_Grid_Search_Deep_Learning_Models_Univariate_Forecasting
    

14. How to Load and Explore Household Energy Usage Data

    * <host-ip>:<port>/lab/workspaces/lab14_Load_and_Explore_Household_Energy_Usage_Data
    

15. How to Develop Naive Models for Multi-step Energy Usage Forecasting

    * <host-ip>:<port>/lab/workspaces/lab15_Naive_Models_Multistep_Energy_Usage_Forecasting
    

16. How to Develop ARIMA Models for Multi-step Energy Usage Forecasting

    * <host-ip>:<port>/lab/workspaces/lab16_ARIMA_Models_Multistep_Energy_Usage_Forecasting
    

17. How to Develop CNNs for Multi-step Energy Usage Forecasting

    * <host-ip>:<port>/lab/workspaces/lab17_CNNs_Multistep_Energy_Usage_Forecasting
    

18. How to Develop LSTMs for Multi-step Energy Usage Forecasting

    * <host-ip>:<port>/lab/workspaces/lab18_LSTMs_Multistep_Energy_Usage_Forecasting
    

19. How to Load and Explore Human Activity Data

    * <host-ip>:<port>/lab/workspaces/lab19_Load_and_Explore_Human_Activity_Data
    

20. How to Develop ML Models for Human Activity Recognition

    * <host-ip>:<port>/lab/workspaces/lab20_ML_Models_Human_Activity_Recognition
    

21. How to Develop CNNs for Human Activity Recognition

    * <host-ip>:<port>/lab/workspaces/lab21_CNNs_Human_Activity_Recognition
    

22. How to Develop LSTMs for Human Activity Recognition

    * <host-ip>:<port>/lab/workspaces/lab22_LSTMs_Human_Activity_Recognition
    

23. Install Deep Learning Libraries

    * <host-ip>:<port>/lab/workspaces/lab23_Setup
    

24. How to Setup Amazon EC2 for Deep Learning on GPUs

    * <host-ip>:<port>/lab/workspaces/lab24_AWS