Deep Learning For Time Series Forecasting

Abstract

1. Introduction

Predict the Future with MLPs, CNNs and LSTMs in Python](https://machinelearningmastery.com/deep-learning-for-time-series-forecasting/) - Jason Brownlee

Sorry for the delay - will try to update the repo soon.

2. Objective

This repository is designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.

This project demonstrates how Deep Learning can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Geo-Joy/Deep-Learning-for-Time-Series-Forecasting.git
cd Deep-Learning-for-Time-Series-Forecasting

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by Geo-Joy and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.