Abstract
The main goal of time series analysis is to establish forecasting model based on past observations and to reduce forecasting error. To achieve these goals, the present paper proposes a new forecasting algorithm based on the fuzzy transform (F-transform) and the fuzzy logical relationships. First, the F-transform is performed based on partitioning of the universe, and the fuzzy logical relationships are employed to forecast. Two experimental applications are used to illustrate and verify the proposed algorithm. The accuracies are evaluated on the basis of average forecasting error percentage and index of agreement to compare the proposed algorithm with other existing methods.
Highlights
One of the main goals of time series analysis is to construct forecasting model that is used to predict future values based on past historical observations
The traditional time series models such as ARIMA and ARCH can not deal with forecasting problems with vague or ambiguous observations represented by linguistic concept
We propose a new algorithm to forecast time series which is based on the fuzzy transform (F-transform) and fuzzy logical relationship
Summary
One of the main goals of time series analysis is to construct forecasting model that is used to predict future values based on past historical observations. The time series models based on the fuzzy set theory can be applied to small sample data which is not easy to handle in traditional data analysis. We propose a new algorithm to forecast time series which is based on the fuzzy transform (F-transform) and fuzzy logical relationship. The F-transform converts original data into weighted mean values where the weights are given by the basic functions which are membership functions to identify fuzzy sets This is a novel method to find an approximation of given data or function. Our proposed algorithm using fuzzy transform as defuzzified value of fuzzy sets corresponding to the partitioned intervals of domain is constructed by fuzzy logical relationships based on fuzzy time series. The proposed fuzzy time series algorithm based on the F-transform allows overlapping membership functions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
