N-Beats architecture for explainable forecasting of multi-dimensional poultry data.
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 po...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0320979 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850139743435620352 |
|---|---|
| author | Baljinder Kaur Manik Rakhra Nonita Sharma Deepak Prashar Leo Mrsic Arfat Ahmad Khan Seifedine Kadry |
| author_facet | Baljinder Kaur Manik Rakhra Nonita Sharma Deepak Prashar Leo Mrsic Arfat Ahmad Khan Seifedine Kadry |
| author_sort | Baljinder Kaur |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0d5fe8b791db4cac9131769d06fda91b |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0d5fe8b791db4cac9131769d06fda91b2025-08-20T02:30:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e032097910.1371/journal.pone.0320979N-Beats architecture for explainable forecasting of multi-dimensional poultry data.Baljinder KaurManik RakhraNonita SharmaDeepak PrasharLeo MrsicArfat Ahmad KhanSeifedine KadryThe 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.https://doi.org/10.1371/journal.pone.0320979 |
| spellingShingle | Baljinder Kaur Manik Rakhra Nonita Sharma Deepak Prashar Leo Mrsic Arfat Ahmad Khan Seifedine Kadry N-Beats architecture for explainable forecasting of multi-dimensional poultry data. PLoS ONE |
| title | N-Beats architecture for explainable forecasting of multi-dimensional poultry data. |
| title_full | N-Beats architecture for explainable forecasting of multi-dimensional poultry data. |
| title_fullStr | N-Beats architecture for explainable forecasting of multi-dimensional poultry data. |
| title_full_unstemmed | N-Beats architecture for explainable forecasting of multi-dimensional poultry data. |
| title_short | N-Beats architecture for explainable forecasting of multi-dimensional poultry data. |
| title_sort | n beats architecture for explainable forecasting of multi dimensional poultry data |
| url | https://doi.org/10.1371/journal.pone.0320979 |
| work_keys_str_mv | AT baljinderkaur nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata AT manikrakhra nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata AT nonitasharma nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata AT deepakprashar nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata AT leomrsic nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata AT arfatahmadkhan nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata AT seifedinekadry nbeatsarchitectureforexplainableforecastingofmultidimensionalpoultrydata |