This repository contains the code and data used for predicting the freight rates of refined oil on the Yangtze River. The project involves various machine learning and statistical models to analyze and forecast the prices.
-
Data
price_data.xlsx
: Contains the historical data of refined oil freight rates.
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Scripts
ARIMA-RF.py
: Implementation of the ARIMA and Random Forest hybrid model.correlation analysis.py
: Performs correlation analysis between different variables.EMD-ARIMA.py
: Empirical Mode Decomposition (EMD) and ARIMA combined model.Kalman-BP.py
: Combines Kalman filter with Back Propagation neural network.LSTM-ARIMA.py
: Hybrid model using Long Short-Term Memory (LSTM) networks and ARIMA.Ridge regression.py
: Implements ridge regression for price prediction.
This model combines the strengths of both ARIMA for capturing linear patterns and Random Forest for capturing non-linear relationships.
This script analyzes the correlation between different features in the dataset to identify significant predictors.
EMD is used to decompose the time series data into Intrinsic Mode Functions (IMFs), and ARIMA is applied to these IMFs for better prediction accuracy.
The Kalman filter is used for noise reduction in the data, and the BP neural network is applied for prediction.
This hybrid model leverages LSTM networks to capture long-term dependencies in the data, while ARIMA is used for short-term forecasting.
Ridge regression is applied to handle multicollinearity and improve the robustness of the prediction model.
- Clone the repository:
git clone https://github.com/yourusername/yangtze-freight-rate-prediction.git
- Navigate to the project directory:
cd yangtze-freight-rate-prediction
- Ensure you have all required dependencies installed. You can use
requirements.txt
if provided:pip install -r requirements.txt
- Run the desired script:
python ARIMA-RF.py
The data used for this project is included in the price_data.xlsx
file. This contains the historical freight rates which are crucial for training and evaluating the models.
The performance of each model is evaluated based on metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. Detailed results and comparisons can be found within the respective scripts.
If you wish to contribute to this project, please fork the repository and create a pull request with your changes. Ensure that your contributions are well-documented and tested.
This project is licensed under the MIT License. See the LICENSE
file for details.
For any inquiries or questions, please contact [email protected].
Feel free to modify the details such as the repository URL, contact email, and any additional instructions or descriptions that are specific to your project.