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  • N-Beats architecture for explainable forecasting of multi-dimensional poultry data [Elektronski vir]
    Kaur, Baljinder ...
    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.
    Vir: PloS one [Elektronski vir]. - ISSN 1932-6203 (e0320979, Vol. 20, iss. 4, 2025, str. 1-19)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2025
    Jezik - angleški
    COBISS.SI-ID - 239347459

    Povezava(-e):

    https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0320979
    DiRROS - Digitalni repozitorij raziskovalnih organizacij Slovenije
    DOI

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