| Naslov: | N-Beats architecture for explainable forecasting of multi-dimensional poultry data |
|---|
| Avtorji: | ID Kaur, Baljinder (Avtor)
ID Rakhra, Manik (Avtor)
ID Sharma, Nonita (Avtor)
ID Prashar, Deepak (Avtor)
ID Mršić, Leo (Avtor)
ID Khan, Arfat Ahmad (Avtor)
ID Kadry, Seifedine (Avtor) |
|---|
| Datoteke: |  URL - Izvorni URL, za dostop obiščite https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0320979
 PDF - Predstavitvena datoteka, prenos (1,63 MB)
MD5: A6246FB7FE3238E1B645229D9D6799B3
|
|---|
| Jezik: | Angleški jezik |
|---|
| Tipologija: | 1.01 - Izvirni znanstveni članek |
|---|
| Organizacija: |  RUDOLFOVO - Rudolfovo – Znanstveno in tehnološko središče Novo mesto
|
|---|
| Povzetek: | 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. |
|---|
| Ključne besede: | poultry, livestock, forecasting, epidemiology, humidity, veterinary diseases, polynomials |
|---|
| Status publikacije: | Objavljeno |
|---|
| Verzija publikacije: | Objavljena publikacija |
|---|
| Datum objave: | 24.04.2025 |
|---|
| Založnik: | Public Library of Science |
|---|
| Leto izida: | 2025 |
|---|
| Št. strani: | str. 1-19 |
|---|
| Številčenje: | Vol. 20, iss. 4 |
|---|
| PID: | 20.500.12556/DiRROS-22649  |
|---|
| UDK: | 519.87:004.92:636.52 |
|---|
| ISSN pri članku: | 1932-6203 |
|---|
| DOI: | 10.1371/journal.pone.0320979 |
|---|
| COBISS.SI-ID: | 239347459 |
|---|
| Avtorske pravice: | © 2025 Kaur et al. |
|---|
| Opomba: | Soavtorji: Manik Rakhra, Nonita Sharma, Deepak Prashar, Leo Mrsic, Arfat Ahmad Khan, Seifedine Kadry;
Nasl. z nasl. zaslona;
Opis vira z dne 13. 6. 2024;
|
|---|
| Datum objave v DiRROS: | 09.09.2025 |
|---|
| Število ogledov: | 276 |
|---|
| Število prenosov: | 109 |
|---|
| Metapodatki: |    |
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
