Fuzzy Time Series For Forecasting Research Articles

A fast and efficient clustering based fuzzy time series algorithm (FEFTS) for regression and classification

Forecasting fuzzy time series (FTS) methods are generally divided into two categories, one is based on intervals of universal set and the other is based on clustering algorithms. Since there are some challenging problems with the interval based algorithms such as the ideal interval length, clustering based FTS algorithms are preferred. Fuzzy Logical Relationships (FLRs) are usually used to establish relationships between input and output data in both interval based and clustering based FTS algorithms. Modeling complicated systems demands high number of FLRs that incurs high runtime to train FTS algorithms. In this study, a fast and efficient clustering based fuzzy time series algorithm (FEFTS) is introduced to handle the regression, and classification problems. Superiority of FEFTS algorithm over other FTS algorithms in terms of runtime and training and testing errors is confirmed by applying the algorithm to various benchmark datasets available on the web. It is shown that FEFTS reduces testing RMSE for regression data up to 40% with the least runtime. Also, FEFTS with the same accuracy as compared to Fuzzy-Firefly classification method, diminishes runtime moderately from 324.33s to 0.0055s.

Enrollment Forecasting based on Modified Weight Fuzzy Time Series

Many different methods and models have been proposed by researchers using fuzzy time series for many different applications. The main issue in forecasting is in improving forecast accuracy. This paper presents the development of weight fuzzy time series based on a collection of variation of the chronological number in the Fuzzy Logical Group (FLG). The aim here is to develop an appropriate weight on fuzzy time series for forecasting of trend series data. A data set of university enrollment for Alabama University and Universiti Teknologi Malaysia (UTM) are used for forecasting. Results from this study shows that the proposed approach gave a lot of improvement. The forecasting fitness function used are the Means Square Error (MSE) and average error.