摘要：The goal of this paper is to propose a new method for fuzzy forecasting of
time series with supervised learning and k-order fuzzy relationships. In the training
phase based on k previous historical periods, a multidimensional matrix of fuzzy
dependencies is constructed. During the test stage, the fitted fuzzy model is run for
validating the observations and each output value is predicted by using a fuzzy input
vector of k previous intervals. The proposed algorithm is verified by a benchmark
dataset for fuzzy time series forecasting. The results obtained are similar or better
than those of other fuzzy time series prediction methods. Comparative analysis shows
the high potential of the new algorithm as an alternative to fuzzy prediction and
reveals some opportunities for its further improvement.
关键词：Fuzzy set; fuzzy time series; forecasting; membership function; fuzzy;
relationships
