There are a variety of approaches to the problem predicting educational enrolment. However, none them can be used when historical data linguistic values. Fuzzy time series is an efficient and effective tool deal with such problems. In this paper, forecast enrolment pre-primary, primary, secondary, tertiary schools in Malaysia carried out using fuzzy approaches. A model developed dataset collect...

The fundamental concept in the theory of fuzzy transform (F-transform) is that of fuzzy partition. The original definition assumes that each two fuzzy subsets overlap in such a way that sum of membership degrees in each point is equal to 1. However, this condition can be generalized to obtain a denser fuzzy partition that leads to improvement of approximation properties of Ftransform. However, ...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

Periodic time series models have become an appealing tool for the analysis of time series showing distinct seasonal patterns. Since these models condition the data{generating mechanism of a given time series on the season they are able to cope with periodic generalisations of common economic models introducing seasonal preferences, seasonal technologies etc. The paper examines for some macroeco...

With the capability of dealing with vague and incomplete data, the study of fuzzy time series has attracted great interest and is expected to expand rapidly. Song and Chissom (1993) first proposed the seven-step forecasting framework of fuzzy time series which are composed of (1) definition of the universe of discourse, (2) partitioning of the universe of discourse, (3) definition of fuzzy sets...

Fuzzy time series model has been employed by many researchers in forecasting activities such as students’ enrolment, temperature fluctuations and stock prices. The existing fuzzy time series models require exact match of the fuzzy logic relationships to calculate the forecasted value. However, in real life applications, the exact match of fuzzy logic relationships is not possible. Thus, an impr...

In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of enrollments at the University of Alabama. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with exist...