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Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network
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| Published in: | PLOS ONE |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2025
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| _version_ | 1864030190036844553 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | PLOS ONE |
| description | |
| discipline_display | Multidisciplinary |
| discipline_facet | Multidisciplinary |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:2984 |
| institution | FRELIP |
| journal_source_facet | PLOS ONE |
| publishDate | 2025 |
| publishDateSort | 2025 |
| record_format | rss_article |
| spellingShingle | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network Multidisciplinary General Multidisciplinary |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Multidisciplinary General Multidisciplinary Multidisciplinary General Multidisciplinary |
| subject_facet | Multidisciplinary General Multidisciplinary |
| title | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| title_auth | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| title_full | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| title_fullStr | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| title_full_unstemmed | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| title_short | Predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in Ontario, Canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| title_sort | predicting the frequency of positive laboratory submissions for porcine reproductive and respiratory syndrome in ontario, canada, using autoregressive integrated moving average, exponential smoothing, random forest, and recurrent neural network |
| topic | Multidisciplinary General Multidisciplinary |
| url | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0339987 |