A time series based monitoring methodology to optimize purchase timing decisions

Kurzfassung/Abstract

Businesses as well as consumers utilize price comparison portals prior to purchasing. Usually such systems provide price time series, but the transformation of the embedded information to select an appropriate purchase time is unknown. We present a methodology to forecast the probability of user customizable sufficient price change events. Four main methodological contributions are presented: (i) an economically meaningful definition of user specified price decreases, (ii) the modification of a bootstrap based ARIMA-GARCH volatility forecasting method to predict the probability of the defined events, (iii) the dynamic statistical evaluation of the forecasting accuracy and (iv) the measurement of the economic utility of the buying recommendation procedure using gain functions. Beyond this, the technique is applied to two distinct forecasting situations, which clearly show the dominance of the proposed decision theoretic framework in comparison to naive purchase strategies like always delaying or always buying immediately.