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

Baljinder Kaur, Manik Rakhra, Nonita Sharma, Deepak Prashar, Leo Mrsic, Arfat Ahmad Khan and Seifedine Kadry

PLOS ONE, 2025, vol. 20, issue 4, 1-19

Abstract: The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework. Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model’s robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0320979

DOI: 10.1371/journal.pone.0320979

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