Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada.

MLA

Petukhova, Tatiana, et al. “Assessment of Autoregressive Integrated Moving Average (ARIMA), Generalized Linear Autoregressive Moving Average (GLARMA), and Random Forest (RF) Time Series Regression Models for Predicting Influenza A Virus Frequency in Swine in Ontario, Canada.” PLoS ONE, vol. 13, no. 5, June 2018, pp. 1–17. EBSCOhost, https://doi.org/10.1371/journal.pone.0198313.

APA

Petukhova, T., Ojkic, D., McEwen, B., Deardon, R., & Poljak, Z. (2018). Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada. PLoS ONE, 13(5), 1–17. https://doi.org/10.1371/journal.pone.0198313

Chicago

Petukhova, Tatiana, Davor Ojkic, Beverly McEwen, Rob Deardon, and Zvonimir Poljak. 2018. “Assessment of Autoregressive Integrated Moving Average (ARIMA), Generalized Linear Autoregressive Moving Average (GLARMA), and Random Forest (RF) Time Series Regression Models for Predicting Influenza A Virus Frequency in Swine in Ontario, Canada.” PLoS ONE 13 (5): 1–17. doi:10.1371/journal.pone.0198313.