Abstract :
This study proposes weighted models to tackle two issues in fuzzy time series forecasting, namely, recurrence and weighting. It is argued that recurrent fuzzy relationships, which were simply ignored in previous studies, should be considered in forecasting. It is also recommended that different weights be assigned to various fuzzy relationships. In previous studies, these fuzzy relationships were treated as if they were equally important, which might not have properly reflected the importance of each individual fuzzy relationship in forecasting. The weighted models are compared with the local regression models in which weight functions also play an important role. Both models are different by nature, but certain theoretical backgrounds in local regression models are adopted. By using the Taiwan stock index as the forecasting target, the empirical results show that the weighted model outperforms one of the conventional fuzzy time series models.
