N-Beats architecture for explainable forecasting of multi-dimensional poultry dataKaur, Baljinder (Avtor) Rakhra, Manik (Avtor) Sharma, Nonita (Avtor) Prashar, Deepak (Avtor) Mršić, Leo (Avtor) Khan, Arfat Ahmad (Avtor) Kadry, Seifedine (Avtor) poultrylivestockforecastingepidemiologyhumidityveterinary diseasespolynomialsThe 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.Public Library of Science20252025-06-14 03:37:07Neznano22649UDK: 519.87:004.92:636.52ISSN pri članku: 1932-6203DOI: 10.1371/journal.pone.0320979COBISS_ID: 239347459sl© 2025 Kaur et al.