Abstract
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.
Highlights
There are a number of ways to obtain forecast value in the analysis of time series [1] such as artificial intelligence approaches [2], artificial neural network (ANN) [3,4] and autoregressive integrated moving average (ARIMA) models [1,5]
Most conventional fuzzy time series (FTS) forecasting models use one variable in forecasting and not all the observations are related to the variable
This research proposes a new approach of type 2 fuzzy time series (T2 FTS) models to exploit an extra observation
Summary
There are a number of ways to obtain forecast value in the analysis of time series [1] such as artificial intelligence approaches [2], artificial neural network (ANN) [3,4] and autoregressive integrated moving average (ARIMA) models [1,5]. The fuzzy time series (FTS) method was widely used in different applications to solve forecasting problems. A new method, type 2 fuzzy time series (T2 FTS) model, was suggested to get the benefit of the related element and solve the forecasting problem indirectly. Type 2 fuzzy time series (T2 FTS) forecasting that is systematic and flexible, together with a reasoning-based model, which is the sliding window method and weighted subsethood-based algorithm, was applied to address this uncertainty. This research proposed a new T2 FTS model to forecast accurate future data values with minimum forecasting error. This research utilized more variable of observations and used type 2 fuzzy time series to forecast the crude palm oil (CPO) prices. The last section of this paper is the summary of this research
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