TITLE:
Fuzzy Time Series Forecasting Based On K-Means Clustering
AUTHORS:
Zhiqiang Zhang, Qiong Zhu
KEYWORDS:
fuzzy time series; fuzzy sets; K-means; enrollments
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.2 No.4B,
January
15,
2013
ABSTRACT: Many
forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades.
These models have been widely applied to various problem domains, especially in
dealing with forecasting problems in which historical data are linguistic
values. In this paper, we present a new
fuzzy time series forecasting model, which uses the historical data as the
universe of discourse and uses the K-means clustering algorithm to cluster the
universe of discourse, then adjust the clusters into intervals. The proposed
method is applied for forecasting University enrollment of Alabama. It is shown
that the proposed model achieves a significant improvement in forecasting
accuracy as compared to other fuzzy time series forecasting models.