USING ARIMA MODELS FOR TURNOVER PREDICTION IN INVESTMENT PROJECT APPRAISAL

Abstract

In the contemporary investment project analyses, most critical point is how to estimate daily turnover of production, or service, based system. In order to make prediction, for investment in certain type of equipment more accurate, daily turnover in the system for automated car wash was observed, along with weather conditions. According to observation, ARIMA model for daily turnover and weather condition is created, according to Box-Jenkins procedure. Conclusion was made that daily turnover can be analytically expressed through daily weather conditions. Validity of observation is checked on second system that is installed in different town in Serbia. According to compared results, conclusion was made that ARIMA model of system daily turnover, predicted by dependent variable, can be generally used as good predictor in investment analyses, or selective criteria for investment decisions.