Environmental Prediction and Imputation Modular Statistical System
EPIMOS (Environmental Prediction and Imputation Modular Statistical System) is a comprehensive framework designed to predict and impute missing environmental variables through a modular statistical approach. This system facilitates the preprocessing, modeling, and prediction of environmental data on different time scales.
- Modular Design: The system is built with a modular approach, making it flexible and extensible.
- Data Preprocessing: Handles raw environmental data and prepares it for modeling and prediction.
- Prediction Module: Provides daily predictions of environmental variables.
- Building Module: Facilitates yearly updates and improvements to the prediction model.
- Imputation: Efficiently fills in missing data points in environmental datasets.
The system follows a multi-module development approach:
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Data Preprocessing Module:
- Processes OASI data. https://www.oasi.ti.ch/web/dati/api.html
- Prepares data for modeling and prediction.
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Building Module (Yearly):
- Updates the model annually to improve accuracy and incorporate new data.
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Prediction Module (Daily):
- Provides daily predictions of environmental variables based on the processed data and updated model.
Contributions are welcome! Please feel free to submit issues and pull requests to improve the system.
This project is licensed under the MIT License. See the LICENSE file for details.