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.
Cite this paper
nullZhang, Z. and Zhu, Q. (2012) Fuzzy Time Series Forecasting Based On K-Means Clustering. Open Journal of Applied Sciences, 2, 100-103. doi: 10.4236/ojapps.2012.24B024.
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