• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.58-63
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• 2022

The daily number of confirmed cases of Coronavirus disease 2019(COVID-19) ranges between 1,000 and 2,000. Despite higher vaccination rates, the number of confirmed cases continues to increase. The Mu variant of COVID-19 reported in some countries by WHO has been identified in Korea. In this study, we predicted the number of confirmed COVID-19 cases in Korea using the SARIMA for the Covid-19 prevention strategy. Trends and seasonality were observed in the data, and the ADF Test and KPSS Test was used accordingly. Order determination of the SARIMA(p,d,q)(P, D, Q, S) model helped in extracting the values of p, d, q, P, D, and Q parameters. After deducing the p and q parameters using ACF and PACF, the data were transformed and schematized into stationary forms through difference, log transformation, and seasonality removal. If seasonality appears, first determine S, then SARIMA P, D, Q, and finally determine ARIMA p, d, q using ACF and PACF for the order excluding seasonality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.