FORECASTING FUZZY TIME SERIES ON A HEURISTIC HIGH-ORDER MODEL

Abstract

ABSTRACT Chen first proposed the high-order fuzzy-time series model to overcome the drawback of existing fuzzy first-order forecasting models. His model involved easy calculations and forecasted more accurately than the other models. This study proposes an enhanced fuzzy-time series model, called heuristic high-order fuzzy time series model, to deal with forecasting problems. The proposed model aims to overcome the deficiency of Chen's model, which depends strongly on the highest-order fuzzy-time series to eliminate ambiguities at forecasting and requires a vast memory for data storage. The empirical analysis reveals that the proposed model yields more accurate forecasts. This work was supported by National Science Council of the Republic of China under Contract No. NSC92-2520-S-194-001.