Fuzzy time series model based on weighted association rule for financial market forecasting

Fuzzy time series have been used to forecast future problems based on historical data. However, previously fuzzy time series methods have some problems: (a) subjectively determined the length of intervals, (b) only discusses the relations for previous period and next period in forecasting stage, and (c) no appropriately consider the weights of fuzzy relations. Based on these reasons, this study proposes a novel fuzzy time series model with 3 focuses: (a) proposing a spread partition algorithm to calculate the length of intervals under the given number of linguistic value and automatically determine the lower bound and upper bound of universe, (b) considering multi‐order fuzzy relation, and (c) building weighted fuzzy association rules based on Apriori's association rules and compute the cardinality of each fuzzy relation. That is, this study proposed a novel method based on Apriori's large itemsets to build the weighted fuzzy time series model for solving forecast problems. To verify the proposed model, the gold price data sets and exchange rates are employed as experimental data sets. This study compared the proposed model with other methods for forecasting accuracy, and the comparison results shown that the proposed method is better than other methods in root mean square error and mean absolute percentage error criteria.