- P-ISSN2765-6934
- E-ISSN2765-7027
- KCI
ISSN : 2765-6934
Prediction of Sales on Some Large-Scale Retailing Types in South Korea
Abstract
Purpose – This paper aims to examine several time series models to predict sales of department stores and discount store markets in South Korea, while other previous trial has performed sales of convenience stores and supermarkets. In addition, optimal predicted values on the underlying model can be got and be applied to distribution industry. Research design, data, and methodology - Two retailing types, under investigation, are homogeneous and comparable in size based on 86 realizations sampled from January 2010 to February in 2017. To accomplish the purpose of this research, both ARIMA model and exponential smoothing methods are, simultaneously, utilized. Furthermore, model-fit measures may be exploited as important tools of the optimal model-building. Results - By applying Holt-Winters’ additive seasonality method to sales of two large-scale retailing types, persisting increasing trend and fluctuation around the constant level with seasonal pattern, respectively, will be predicted from May in 2017 to February in 2018. Conclusions - Considering 2017-2018 forecasts for sales of two large-scale retailing types, it is important to predict future sales magnitude and to produce the useful information for reforming financial conditions and related policies, so that the impacts of any marketing or management scheme can be compared against the do-nothing scenario.
- keywords
- Large-Scale Retailing Type, ARIMA Model, Exponential Smoothing Method, Optimal Forecasts.
Reference
Akaike, H. (1970). Statistical predictor identification. Annals of the Institute of Statistical Mathematics, 22, 203-217.
Anderson, J. R. (1994). Simpler exponentially weighted moving averages with irregular updating periods. Journal of the Operational Research Society, 45, 486.
Anderson, T. W. (1971). The statistical analysis of time series. New York: Wiley.
Andrews, R. L. (1994). Forecasting performance of structural time series models. Journal of Business and Economic Statistics, 12, 129-133.
Archibald, B. C. (1990). Parameter space of the Holt-Winters’ model. International Journal of Forecasting. 6, 199-209.
Archibald, B. C., & Koehler, A. B. (2003). Normalization of seasonal factors in Winters’ methods. International Journal of Forecasting, 19, 143-148.
Bartolomei, S. M., & Sweet, A. L. (1989). A note on a comparison of exponential smoothing methods for forecasting seasonal series. International Journal of Forecasting, 5, 111-116.
Bianchi, L., Jarrett, J., & Hanumara, R. C. (1998). Improving forecasting for telemarketing centers by ARIMA modeling with intervention. International Journal of Forecasting, 14, 497-504.
Bowerman, B. L., O’Connell, R., & Koehler, A. B. (2005). Forecasting, time series, and regression(4th edition). Pacific Grove, CA: Duxbury Press.
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: Forecasting and control (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
Brown, R. G. (1959). Statistical forecasting for inventory control. New York: McGraw-Hill.
Brown, R. G. (1963). Smoothing, forecasting and prediction of discrete time series. Englewood Cliffs, NJ: Prentice-Hall.
Broze, L., & Mélard, G. (1990). Exponential smoothing:Estimation by maximum likelihood. Journal of Forecasting, 9, 445-455.
Chatfield, C. (1988). What is the ‘best method’ of forecasting?. Journal of Applied Statistics, 15, 19-38.
Chatfield, C. (1993). Calculating interval forecasts. Journal of Business and Economic Statistics, 11, 121-135.
Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society, Series A, 158, 419-466.
Chatfield, C. (1996). Model uncertainty and forecast accuracy. Journal of Forecasting, 15, 495-508.
Chatfield, C. (1997). Forecasting in the 1990s. Journal of the Royal Statistical Society, Series D, 46, 461-473.
Chatfield, C. (2002). Confessions of a pragmatic statistician. Journal of the Royal Statistical Society, Series D, 51, 1-20.
Chio, E. Y.(2016). The effects of household debt on household consumption through quantile regression analysis. Journal of Human and Social Science, 17(1), 589-613.
Fuller, W. A. (1976). Introduction to statistical time series. New York: John Wiley & Sons, Inc.
Gardner, E. S. Jr. (1985). Exponential smoothing: the state of the art. Journal of Forecasting, 4, 1–28.
Gardner, E. S. Jr. (2006). Exponential smoothing: The state of the art Part II. International Journal of Forecasting, 22, 637–677.
Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.
Han, S. H., Yang, H. C., & Kim, J. L.(2015). The impact of service quality on service satisfaction and store loyalty: Service value as a moderator. Journal of Distribution Science, 13(10), 101-108.
Hatemilton, J, A. (2004). Multivariate tests for autocorrelation in the stable and unstable VAR models. Economic Modelling, 21(4), 661–683.
Holt, C. C. (1957). Forecasting seasonality and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10.
Hurvich, C. M., & Tsai, C. L. (1990). The impact of model selection on inference in linear regression. American Statistician, 44, 214–217.
Hwang, J. Y., & Lee, S. H. (2015). Household debt and its impacts on consumption and income in Korea. Financial Policy Reviews, 17(2), 127-153.
Jeong, D. B. (2009). Demanding forecasting of time series I. Seoul, Korea: Hannarae Academy.
Jeong, D. B. (2010). Demanding forecasting of time series I. Seoul, Korea: Hannarae Academy.
Jeong, D. B. (2016). The degree of association between traditional markets and related major factors in Korea. Journal of Distribution Science, 14(7), 5-14.
Jeong, D. B. (2016). Optimal forecasting for sales at convenience stores in Korea using seasonal ARIMA-Intervention model. Journal of Distribution Science, 14(11), 83-90.
Kim, S. M., Ahn, J. S., & Shim, G. E.(2014). Critical factors for sales of department stores - focused on comparison of influence on sales between location and non-location factors. Journal of Urban Design Institute of Korea, 15(1), 51-66.
Lee, S. N. (2016). An empirical study on affecting factors of household debts. Journal of Digital Convergence, 14(5), 177-183.
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65, 297-303.
Pankratz, A. (1983). Forecasting with univariate Box-Jenkins models: Concepts and cases. New York: John Wiley & Sons, Inc.
Pankratz, A. (1991). Forecasting with dynamic regression models. New York: John Wiley & Sons, Inc.
Roberts, S. A. (1982). A general class of Holt-Winters type forecasting models. Management Science, 28, 808-820.
Rosanna, R. J., & Seater, J. J. (1995). Temporal aggregation and economic time series. Journal of Business and Economic Statistics, 13, 441-451.
Rosas, A. L., & Guerrero, V. M. (1994). Restricted forecasts using exponential smoothing techniques. International Journal of Forecasting, 10, 515-527.
Tsay, R. S., & Tiao, G. C. (1984). Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARMA Models. Journal of the American Statistical Association, 79, 84-96.
Trigg, D. W., & Leach, D. H. (1967). Exponential smoothing with an adaptive response rate. Operational Research Quarterly, 18, 53-59.
Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324–342.
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