1 research outputs found
Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting
Many models and techniques have been proposed by researchers to improve forecasting accuracy
using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy
function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by
considering new factor value in establishing the forecasting model without any probabilistic
approaches. The proposed model was evaluated by comparing its performance with inverse and non�inverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such
as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly
car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the
proposed model has potential to improve the forecasting accuracy compared to the existing inverse
and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast
values using the systematic rules.
Keywords: Fuzzy time series, inverse fuzzy function, non-probabilistic model, non-inverse fuzzy
model, future forecas