A FCM-based deterministic forecasting model for fuzzy time series

Abstract

a b s t r a c tThe study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such a deficiency was recently solved by Li and Cheng [S.-T. Li, Y.-C. Cheng, Deterministic Fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications 53 (2007) 1904-1920] using a deterministic forecasting method. In this paper, we propose a novel forecasting model to enhance forecasting functionality and allow processing of two-factor forecasting problems. In addition, this model applies fuzzy cmeans (FCM) clustering to deal with interval partitioning, which takes the nature of data points into account and produces unequal-sized intervals. Furthermore, in order to cope with the randomness of initially assigned membership degrees of FCM clustering, Monte Carlo simulations are used to justify the reliability of the proposed model. The superior accuracy of the proposed model is demonstrated by experiments comparing it to other existing models using real-world empirical data.

A combined robust fuzzy time series method for prediction of time series

“…Lu et al [76] carried out a model for interval prediction based on granules information. In some studies fuzzy C-means (FCM) technique was used by Cheng et al [38], Li et al [74], Aladag et al [6], Alpaslan et al [8], Egrioglu [45], Egrioglu et al [53] and Sun et al [85]. While Askari et al [11] and Askari and Montazerin [10] introduced forecasting models used fuzzy clustering algorithms, Wang and [92] presented an approach based on automatic clustering.…”

A Hybrid Model for Forecasting Groundwater Levels Based on Fuzzy C-Mean Clustering and Singular Spectrum Analysis

“…Song and Chissom [18] first introduced the definition of fuzzy time series as follows [19]: "Let X(t) ∈ R 1 , t = 0, 1, 2, . .…”

“…Song and Chissom [18] defined fuzzy relations among fuzzy time series, which are based on the assumption that the values of fuzzy time series F(t) are fuzzy sets, and the observation of time t is caused by the observations of the previous times [19].…”

The Forecasting Of Labour Force Participation And The Unemployment Rate In Poland And Turkey Using Fuzzy Time Series Methods

“…In some of them, fixed lengths were determined arbitrarily Chissom 1993a,b, 1994;Chen 1996Chen , 2002. In the others, a fuzzy C-means (FCM) procedure was used for fuzzification Li et al 2008;Cagcag Yolcu 2013).…”