CALENDAR VARIATION MODEL FOR FORECASTING TIME SERIES DATA WITH ISLAMIC CALENDAR EFFECT
Keywords: Calendar Variation model, Islamic Calendar, time series
Abstract
The aim of this paper is to develop a statistical model for explaining and forecasting the time series that contains Islamic Calendar effect. In time series literature, calendar variation is defined as a periodic and recurrent pattern with variation length period that usually caused by cultures and religions of people in a certain area. In Indonesia, the effect of the Eids holiday in many daily activities, such as transportation, inflation and consumption, is one example of calendar variations. This holiday happens on different month after three years or shift to previous month after at the same month on three years. This paper evaluates the disadvantage of seasonal classical time series model, such as Winter’s, Decomposition and ARIMA models, and develops a Calendar Variation model for forecasting time series that contain Islamic Calendar Effect. In this research, a real data about monthly sales of sardines are used as a case study. The results show that classical time series models, such as Winter’s, Decomposition and ARIMA models, cannot describe the calendar variation effect and yield invalid and unreliable forecast, particularly at the time (month) when the calendar variation happens. On the contrary, Calendar Variation model is a model that can explain precisely the impact of the calendar variation effect and gives valid and reliable forecasts.
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References
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Bokil, M. & Schimmelpfennig, A. (2005). Three attempts at inflation forecasting in Pakistan. IMF Working Paper, WP/05/105.
Bowerman, B.L. & O’Connel, D. (1993). Forecasting and time series: An applied approach, 3rd ed. California: Duxbury Press.
Box, G.E.P. & Jenkins, G.M. (1976). Time series analysis: Forecasting and control. San Fransisco: Holden-Day, Revised edn.
Box, G.E.P., Jenkins, G.M., & Reinsel, G.C. (1994). Time series analysis, forecasting and Control, 3rd edition. Prentice Hall, Englewood Cliffs.
Cryer, J.D. (1986). Time series analysis. Boston: Publishing Comp.
Faraway, J. & Chatfield, C. (1998). Time series forecasting with neural network: a comparative study using the airline data. Applied Statistics, 47, 231–250.
Hanke, J.E. & Reitsch, A.G. (1995). Business forecasting. NJ: Prentice Hall, Englewood Cliffs.
Makridakis, S. & Wheelwright, S.C. (1987). The handbook of forecasting: A manager’s guide, 2nd Edition. New York: John Wiley & Sons Inc.
Suhartono & Sampurno, B.S. (2002). Comparison study between Transfer Function and Intervention-Calendar Variation models for forecasting train and plane passengers. Jurnal Matematika atau Pembelajarannya, Special Ed. Malang: UNM, Indonesia.
Suhartono, Subanar, & Guritno, S. (2005a). The Impact of Data Preprocessing on Feedforward Neural Networks Model for Forecasting Trend and Seasonal Time Series. Proceeding The International Conference on Applied Mathematics (ICAM05). Bandung: Institut Teknologi Bandung.
Suhartono, Subanar, & Guritno, S. (2005b), Modeling of financial data by using Feedforward Neural Networks, Proceeding The International Conference on Applied Mathematics (ICAM05). Bandung: Institut Teknologi Bandung.
Sullivan, R., Timmermann, A., & White, H. (1998), Danger of data driven inference: The case of calendar effects in stock returns. UCSD Working Paper.
Wei, W.W.S. (1990). Time series analysis: Univariate and multi-variate methods. USA: Addison-Wesley Publishing Co.
Published
Aug 15, 2006
How to Cite
Suhartono. (2006). CALENDAR VARIATION MODEL FOR FORECASTING TIME SERIES DATA WITH ISLAMIC CALENDAR EFFECT. Jurnal Matematika Sains Dan Teknologi, 7(2), 85–94. Retrieved from https://jurnal.ut.ac.id/index.php/jmst/article/view/636
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