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RETRACTED: N-Beats architecture for explainable forecasting of multi-dimensional poultry data.

Baljinder Kaur¹, Manik Rakhra¹, Nonita Sharma²

¹Department of Computer Science & Engineering, Lovely Professional University, Phagwara, Punjab, India.

|April 24, 2025

Summary

This study introduces N-BEATS for poultry data forecasting, enhancing farm management with interpretable predictions. The novel approach outperforms traditional models, improving accuracy in agricultural analytics.

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Area of Science:

Agricultural Science
Data Science
Artificial Intelligence

Background:

Poultry production is vital to the agricultural economy, necessitating accurate data forecasting.
Optimizing revenue, resource use, and productivity hinges on reliable poultry data predictions.

Purpose of the Study:

To introduce a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting.
To enhance forecast interpretability using an integrated Explainable AI (XAI) framework.
To improve decision-making in poultry farm management through transparent and interpretable forecasts.

Main Methods:

Applied the N-BEATS architecture for time series modeling.
Utilized a multivariate dataset of environmental parameters for poultry disease diagnostics.
Integrated an Explainable AI (XAI) framework for enhanced interpretability.

Main Results:

N-BEATS outperformed conventional deep learning models (LSTM, GRU, RNN, CNN).
Achieved low error metrics: MAE (0.172), RMSE (0.313), MSLE (0.042), RMSLE (0.204).
Demonstrated robustness with a positive R-squared value (0.034), indicating superior performance.

Conclusions:

N-BEATS is a superior and interpretable solution for complex, multi-dimensional forecasting in poultry production.
The findings have significant implications for enhancing predictive analytics in agriculture.
This approach offers a robust method for optimizing poultry farm management.