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Open AccessArticle

Type 2 Fuzzy Inference-Based Time Series Model

Centre for Pre University Studies, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak 94300, Malaysia
Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
Faculty of Engineering, Universitas Islam Riau, Pekan Baru, Riau 28284, Indonesia
Author to whom correspondence should be addressed.
Symmetry 2019, 11(11), 1340;
Received: 25 September 2019 / Revised: 18 October 2019 / Accepted: 22 October 2019 / Published: 31 October 2019
Fuzzy techniques have been suggested as useful method for forecasting performance. However, its dependency on experts’ knowledge causes difficulties in information extraction and data collection. Therefore, to overcome the difficulties, this research proposed a new type 2 fuzzy time series (T2FTS) forecasting model. The T2FTS model was used to exploit more information in time series forecasting. The concepts of sliding window method (SWM) and fuzzy rule-based systems (FRBS) were incorporated in the utilization of T2FTS to obtain forecasting values. A sliding window method was proposed to find a proper and systematic measurement for predicting the number of class intervals. Furthermore, the weighted subsethood-based algorithm was applied in developing fuzzy IF–THEN rules, where it was later used to perform forecasting. This approach provides inferences based on how people think and make judgments. In this research, the data sets from previous studies of crude palm oil prices were used to further analyze and validate the proposed model. With suitable class intervals and fuzzy rules generated, the forecasting values obtained were more precise and closer to the actual values. The findings of this paper proved that the proposed forecasting method could be used as an alternative for improved forecasting of sustainable crude palm oil prices. View Full-Text
Keywords: fuzzy time series; reasoning-based model; sliding window method; type 2 fuzzy time series; weighted subsethood-based algorithm fuzzy time series; reasoning-based model; sliding window method; type 2 fuzzy time series; weighted subsethood-based algorithm
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Rahim, N.F.; Othman, M.; Sokkalingam, R.; Abdul Kadir, E. Type 2 Fuzzy Inference-Based Time Series Model. Symmetry 2019, 11, 1340.

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