A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques

A novel forecasting method based on multi-order fuzzy time series and technical analysis

“…From Tables 4 and 5 we can observe that, although our predicted average RMSE from 1999 to 2004 is a bit higher than the result in [53] , all of the other average RMSEs are smaller than those of the existing works in [3,26,[29][30][31]40,46,47,52,53] . Because of the multivariate strategy, our predicted results tend to be conservative.…”

“…In addition, we can observe that the improvement of the results becomes more and more difficult beginning from the fourth model (Chen et al . 's method [31] ).…”

“…The data from January to October are used as the training set, and the data from November to December are used as the testing set to verify the performance of the model. The compared models include not only fuzzy time series models but also neural network models [3,26,[29][30][31]40,46,47,52,53] . RMSE is used to evaluate the performance of the proposed method.…”

“…Chen and Chen [30] investigated TAIEX forecasting with fuzzy time series by considering main factor and secondary factor. Chen et al [31] used a particle swarm optimization algorithm to improve their research. Kim et al [32] used technical indicators to construct multiple classifiers for predicting a stock price index.…”

“…[15,16,18,19,[29][30][31]49] , Yu and Huarng [4,9,[26][27][28]39,45] did a lot of excellent works. Recently, Wei et al .…”