Abstract
GA TCN LSTM is an open-source Data Science project. An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting. This project focuses on developing a forecasting model for oil production using advanced machine learning techniques and optimization algorithms. The project includes the development of a Genetic Algorithm- Temporal Convolutional Neural Network- Long Short-Term Memory (GA-TCN-LSTM) ensemble model, as well as benchmarking against conventional models such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Temporal Convolutional Network (TCN). It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
This project focuses on developing a forecasting model for oil production using advanced machine learning techniques and optimization algorithms. The project includes the development of a Genetic Algorithm- Temporal Convolutional Neural Network- Long Short-Term Memory (GA-TCN-LSTM) ensemble model, as well as benchmarking against conventional models such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Temporal Convolutional Network (TCN). For more details on the methodology and techniques used in this project, please read the preprint paper.
Oil production forecasting is a critical task for many oil and gas companies, governments, and policy-makers. Accurate forecasts are essential for planning and decision-making, such as determining production rates, managing inventory, and estimating future revenue.
Conventional oil production forecasting methods have limitations due to complex data, high uncertainty, and failure to reflect the actual system and dynamic changes.. Therefore, the use of advanced machine learning techniques and optimization algorithms can improve the accuracy of forecasts by accounting for these complexities and identifying the optimal combination of hyperparameters for each model. This project aims to provide decision-makers with better information to make informed decisions and improve the overall forecasting process.
2. Objective
An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.
This project demonstrates how Jupyter Notebook 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.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/ashrafalaghbari/GA-TCN-LSTM.git
cd GA-TCN-LSTMFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by ashrafalaghbari and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
Want to build this as your internship project?
Work on a Data Science project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.
Apply for Data Science Internship