Modeling and forecasting of meat and eggs produsing in Ukraine with seasonal ARIMA-model
Abstract
The article investigates the problem of seasonality in the production of meat and eggs on the basis of the dynamics of time series of meat and egg production in Ukraine in 2009–2016. After the construction of the input time series in the stationary, parameters of the model were found and the production volumes of meat and eggs for the subsequent periods are predicted. The seasonal autoregressive economic and mathematical models such as SARIMA (seasonally ARIMA) were fitted on the basis of the time series describing the monthly dynamics of meat and egg production in Ukraine (based on statistics for the period 2009–2016). On the basis of these models, the forecasts of these indicators are received for the next two years. By comparing the obtained forecasts with the actual values, the conclusion is drawn about the adequacy of the results obtained.
References
Список використаних джерел
Winters P. R. Forecasting sales by exponentially weighted moving averages / P. R. Winters // Management science. – 1960. – № 6(3). – Pp. 324–342.
Theil H. Some observations on adaptive forecasting / H. Theil, S. Wage // Management Science. – 1960. – № 10(2). – Рp. 198–206.
Harrison P. J. Exponential smoothing and short-term sales forecasting / P. J. Harrison // Management Science. – 1967. – № 13(11). – Рp. 821–842.
Box G. E. Time series analysis : forecasting and control / G. E. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung. – New Jersey : John Wiley & Sons. – 2015.
Findley D. F. Illuminating ARIMA model-based seasonal adjustment with three fundamental seasonal models / D. F. Findley, D. P. Lytras, A. Maravall // SERIEs. – 2016. – № 7(1). – Рp. 11–52.
Han P. Application of the ARIMA models in drought forecasting using the standardized precipitation index / P. Han, P. Wang, M. Tian, S. Zhang, J. Liu, D. Zhu // 6th Computer and Computing Technologies in Agriculture (CCTA). – 2012. –Part I. – Pp. 352–358.
Moeeni H. Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction / H. Moeeni, H. Bonakdari, I. Ebtehaj // Water Resources Management. – 2017. – № 31(7). – Р. 2141–2156. https://doi.org/10.1007/s11269-017-1632-7.
Iqbal N. Use of the ARIMA model for forecasting wheat area and production in Pakistan / N. Iqbal, K. Bakhsh, A. Maqbool, A. S. Ahmad // Journal of Agriculture and Social Sciences. – 2005. – № 1(2). – Рp. 120–122.
Nochai R. ARIMA model for forecasting oil palm price / R. Nochai, T. Nochai // Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications. – 2006. – University Saints Malaysia, Penang, Pp. 13–15.
Padhan P. C. Application of ARIMA model for forecasting agricultural productivity in India / P. C. Padhan // Journal of Agriculture and Social Sciences. – 2012. – № 8(2). – Pp. 50–56.
Martín‐Rodríguez G. Forecasting pseudo‐periodic seasonal patterns in agricultural prices / G. Martín‐Rodríguez, J. J. Cáceres‐Hernández // Agricultural Economics. – 2012. – № 43(5). – Рp. 531–544. https://doi.org/10.1111/j.1574-0862.2012.00601.x.
Державна служба статистики України [Електронний ресурс]. – Режим доступу : http://www.ukrstat.gov.ua.
Brockwell P. J. Introduction to time series and forecasting / P. J. Brockwell, R. A. Davis. – Zurich : Springer. – 2016. https://doi.org/10.1007/978-3-319-29854-2.
Hyndman R. J. Forecasting: principles and practice [Electronic resource] / R. J. Hyndman, G. Athanasopoulos. – Otexts. – 2014. – Mode of access : http://otexts.org/fpp2/?__utma=1.1898273802.1505861613.1505861613.1505861613.1&__utmb=1.4.10.1505861613&__utmc=1&__utmx=-&__utmz=1.1505861613.1.1. utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=(not%20provided)&__utmv=-&__utmk=108819714.
The R Project for Statistical Computing [Electronic resource]. – Mode of access : https://www.r-project.org.
Shumway R. H. Time series analysis and its applications / R. H. Shumway, D. S. Stoffer // Studies In Informatics And Control. – 2000. – № 9(4). – Рp. 375–376.
References
Winters, P. R. (1960), Forecasting sales by exponentially weighted moving averages. Management science, no. 6(3), pp. 324–342.
Theil, H. and Wage, S. (1964), Some observations on adaptive forecasting. Management Science, no. 10(2), pp. 198–206.
Harrison, P. J. (1967), Exponential smoothing and short-term sales forecasting. Management Science, no. 13(11), pp. 821–842.
Box, G. E., Jenkins, G. M., Reinsel, G. C. and Ljung, G. M. (2015), Time series analysis: forecasting and control. John Wiley & Sons, New Jersey, USA.
Findley, D. F., Lytras, D. P. and Maravall, A. (2016), Illuminating ARIMA model-based seasonal adjustment with three fundamental seasonal models. SERIEs, no. 7(1), pp. 11–52.
Han, P., Wang, P., Tian, M., Zhang, S., Liu, J. and Zhu, D. (2012), Application of the ARIMA models in drought forecasting using the standardized precipitation index. In 6th Computer and Computing Technologies in Agriculture (CCTA), no. Part I, Springer, Zhangjiajia, China, pp. 352–358.
Moeeni, H., Bonakdari, H. and Ebtehaj, I. (2017), Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction. Water Resources Management, no. 31(7), pp. 2141–2156. https://doi.org/10.1007/s11269-017-1632-7.
Iqbal, N., Bakhsh, K., Maqbool, A. and Ahmad, A. S. (2005), Use of the ARIMA model for forecasting wheat area and production in Pakistan. Journal of Agriculture and Social Sciences, no. 1(2), pp. 120–122.
Nochai, R. and Nochai, T. (2006), ARIMA model for forecasting oil palm price. In Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Univercity Saints Malaysia, Penang, June, pp. 13–15.
Padhan, P. C. (2012), Application of ARIMA model for forecasting agricultural productivity in India. Journal of Agriculture and Social Sciences, no. 8(2), pp. 50–56.
Martín-Rodríguez, G. and Cáceres-Hernández, J. J. (2012), Forecasting pseudo‐periodic seasonal patterns in agricultural prices. Agricultural Economics, no. 43(5), pp. 531–544. https://doi.org/10.1111/j.1574-0862.2012.00601.x.
The official site of State Statistics Service of Ukraine (2015), available at: http://www.ukrstat.gov.ua.
Brockwell, P. J. and Davis, R. A. (2016), Introduction to time series and forecasting. Springer, Zurich, Switzerland. https://doi.org/10.1007/978-3-319-29854-2.
Hyndman, R. J. and Athanasopoulos, G. (2014), Forecasting: principles and practice. OTexts. [Online], available at: http://otexts.org/fpp2/?__utma=1.1898273802.1505861613.1505861613.1505861613.1&__utmb=1.4.10.1505861613&__utmc=1&__utmx=-&__utmz=1.1505861613.1.1. utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=(not%20provided)&__utmv=-&__utmk=108819714.
The R Project for Statistical Computing [Online], available at: https://www.r-project.org.
Shumway, R. H. and Stoffer, D. S. (2000), Time series analysis and its applications. Studies In Informatics And Control, no. 9(4), pp. 375–376.
