{"id":27453643,"url":"https://github.com/jasontan22/aefes-time-series-forecasting","last_synced_at":"2025-08-09T16:27:33.945Z","repository":{"id":287993242,"uuid":"966463953","full_name":"jasontan22/aefes-time-series-forecasting","owner":"jasontan22","description":"Bu proje, Anadolu Efes Biracılık ve Malt Sanayii A.Ş. (AEFES) piyasa verilerini kullanarak kapanış fiyatlarının gelecekteki değerlerini tahmin etmek amacıyla derin öğrenme yöntemleri (LSTM, BiLSTM, CNN+LSTM) kullanmaktadır. Projede, veri ön işleme, model eğitimi ve değerlendirme adımları detaylandırılmıştır.","archived":false,"fork":false,"pushed_at":"2025-07-10T05:44:38.000Z","size":394,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-10T06:42:05.417Z","etag":null,"topics":["bilstm","cnn-lstm","data-analysis","deep-learning","financial-forecasting","lstm","machine-learning","python","stock-price-prediction","tensorflow"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jasontan22.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-15T00:56:46.000Z","updated_at":"2025-07-10T05:44:41.000Z","dependencies_parsed_at":"2025-04-15T13:56:28.964Z","dependency_job_id":"0a55187f-c52e-41ab-877f-0de0d2a65b3e","html_url":"https://github.com/jasontan22/aefes-time-series-forecasting","commit_stats":null,"previous_names":["jasontan22/aefes-time-series-forecasting"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/jasontan22/aefes-time-series-forecasting","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasontan22%2Faefes-time-series-forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasontan22%2Faefes-time-series-forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasontan22%2Faefes-time-series-forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasontan22%2Faefes-time-series-forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jasontan22","download_url":"https://codeload.github.com/jasontan22/aefes-time-series-forecasting/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jasontan22%2Faefes-time-series-forecasting/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264619728,"owners_count":23638508,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bilstm","cnn-lstm","data-analysis","deep-learning","financial-forecasting","lstm","machine-learning","python","stock-price-prediction","tensorflow"],"created_at":"2025-04-15T13:56:27.175Z","updated_at":"2025-07-10T17:33:53.552Z","avatar_url":"https://github.com/jasontan22.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AEFES Time Series Forecasting 📈\n\n![GitHub release](https://img.shields.io/github/release/jasontan22/aefes-time-series-forecasting.svg)\n![Python](https://img.shields.io/badge/python-3.8%2B-blue.svg)\n![TensorFlow](https://img.shields.io/badge/tensorflow-2.4%2B-orange.svg)\n\nWelcome to the AEFES Time Series Forecasting repository! This project uses market data from Anadolu Efes Biracılık ve Malt Sanayii A.Ş. (AEFES) to predict future closing prices using deep learning techniques such as LSTM, BiLSTM, and CNN+LSTM. \n\nYou can find the latest releases of this project [here](https://github.com/jasontan22/aefes-time-series-forecasting/releases). Make sure to download the files and execute them for a hands-on experience.\n\n## Table of Contents\n\n1. [Project Overview](#project-overview)\n2. [Technologies Used](#technologies-used)\n3. [Installation](#installation)\n4. [Usage](#usage)\n5. [Data Preprocessing](#data-preprocessing)\n6. [Model Training](#model-training)\n7. [Evaluation](#evaluation)\n8. [Contributing](#contributing)\n9. [License](#license)\n10. [Contact](#contact)\n\n## Project Overview\n\nThis project aims to forecast the closing prices of AEFES stocks. By utilizing deep learning models, we can analyze historical data and make informed predictions about future prices. The main components of the project include:\n\n- Data collection and preprocessing\n- Building and training models\n- Evaluating model performance\n- Making predictions\n\n## Technologies Used\n\nThis project employs several technologies and libraries, including:\n\n- **Python**: The main programming language for this project.\n- **TensorFlow**: A powerful library for building and training deep learning models.\n- **Pandas**: For data manipulation and analysis.\n- **NumPy**: For numerical operations.\n- **Matplotlib**: For data visualization.\n- **Seaborn**: For statistical data visualization.\n\n## Installation\n\nTo set up this project on your local machine, follow these steps:\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/jasontan22/aefes-time-series-forecasting.git\n   ```\n\n2. Navigate to the project directory:\n\n   ```bash\n   cd aefes-time-series-forecasting\n   ```\n\n3. Install the required packages:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Usage\n\nTo use the forecasting models, you need to execute the main script. Here’s how to do it:\n\n1. Open your terminal.\n2. Navigate to the project directory if you haven’t already.\n3. Run the script:\n\n   ```bash\n   python main.py\n   ```\n\nThe script will execute the data preprocessing, model training, and evaluation steps automatically.\n\n## Data Preprocessing\n\nData preprocessing is a crucial step in any machine learning project. In this project, we perform the following tasks:\n\n1. **Data Collection**: We gather historical stock prices from a reliable source.\n2. **Cleaning Data**: We handle missing values and remove any anomalies.\n3. **Normalization**: We scale the data to improve model performance.\n4. **Splitting Data**: We divide the dataset into training and testing sets.\n\nThe code for data preprocessing can be found in the `data_preprocessing.py` file.\n\n## Model Training\n\nIn this project, we explore various deep learning models:\n\n### LSTM (Long Short-Term Memory)\n\nLSTM networks are effective for time series forecasting due to their ability to remember long-term dependencies. We build an LSTM model using TensorFlow.\n\n### BiLSTM (Bidirectional LSTM)\n\nBiLSTM extends LSTM by processing data in both forward and backward directions. This can capture more context from the data.\n\n### CNN+LSTM\n\nThis model combines Convolutional Neural Networks (CNN) with LSTM to extract features from time series data before making predictions.\n\nYou can find the implementation details in the `model_training.py` file.\n\n## Evaluation\n\nAfter training the models, we evaluate their performance using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). We visualize the predictions against actual values to assess the model's accuracy.\n\nThe evaluation code is located in the `evaluation.py` file.\n\n## Contributing\n\nWe welcome contributions to improve this project. If you want to contribute, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch for your feature or bug fix.\n3. Make your changes and commit them.\n4. Push to your branch.\n5. Create a pull request.\n\nPlease ensure your code adheres to the existing style and includes appropriate tests.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Contact\n\nFor any questions or suggestions, feel free to reach out:\n\n- **Email**: your.email@example.com\n- **GitHub**: [jasontan22](https://github.com/jasontan22)\n\nYou can find the latest releases of this project [here](https://github.com/jasontan22/aefes-time-series-forecasting/releases). Download the files and execute them to explore the capabilities of this forecasting model.\n\nThank you for visiting the AEFES Time Series Forecasting repository! Happy coding!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjasontan22%2Faefes-time-series-forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjasontan22%2Faefes-time-series-forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjasontan22%2Faefes-time-series-forecasting/lists"}