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| # LSTM_HHO | ||
| LSTM with Harris Hawks optimization (HHO) | ||
| # LSTM Model with Harris Hawks Optimization for Time Series Forecasting | ||
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| This repository provides a framework for using an LSTM (Long Short-Term Memory) neural network, optimized with the Harris Hawks Optimization (HHO) algorithm, to forecast time series data. This setup is flexible and allows users to input their own CSV datasets for forecasting a target variable of their choice. | ||
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| ## Table of Contents | ||
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| - [Project Description](#project-description) | ||
| - [Dataset](#dataset) | ||
| - [Installation](#installation) | ||
| - [Usage](#usage) | ||
| - [Results](#results) | ||
| - [Contributing](#contributing) | ||
| - [License](#license) | ||
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| ## Project Description | ||
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| The project aims to forecast a specified target variable in time series data using an LSTM neural network, a powerful tool for capturing temporal dependencies. To enhance the model's performance, the Harris Hawks Optimization (HHO) algorithm is used to fine-tune hyperparameters such as the number of LSTM units and batch size. | ||
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| ## Dataset | ||
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| Users can utilize their own dataset in CSV format. The dataset should include: | ||
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| - A `datetime` column to be used as the index. | ||
| - One or more feature columns that provide data related to the target variable. | ||
| - A target variable column that you wish to predict. The name of this column can be customized in the code. | ||
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| Ensure that your dataset is properly preprocessed, including handling any missing values and scaling numerical features if necessary. | ||
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| ## Installation | ||
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| To set up the environment for this project, follow these steps: | ||
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| 1. **Clone the repository**: | ||
| ```bash | ||
| git clone https://github.com/your-username/your-repo-name.git | ||
| cd your-repo-name | ||
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| 2. **Create a virtual environment (optional but recommended):** | ||
| ```bash | ||
| python -m venv env | ||
| source env/bin/activate # On Windows use `env\Scripts\activate` | ||
| 4. **install the required packages:** | ||
| ```bash | ||
| pip install -r requirements.txt |