Hybrid heuristic model and Fuzzy C-Means for stock forecasting using Type 2 Fuzzy Time Series

Forecasting is important in investment because of the inconsistent stock price pattern that requires in-depth analysis. This study proposes using a combination of heuristic and Fuzzy C-Means (FCM) models on Fuzzy Time Series Type 2. This study aims to obtain accurate forecasting results by using more data from the time series. The results show that the proposed model provides accurate forecasting. The FCM model is used to group data into clusters and form intervals. Heuristics also optimizes the performance of Fuzzy Logical Relationships Group (FLRG) by using up and down trends. Type 2 FTS is an extension of Type 1 that uses union and intersection operators to refine fuzzy relations. The results show that the modification by combining FCM and heuristics in Type 2 FTS for stock forecasting provides excellent results with a MAPE value of 2,87%.