Forecasting model based on heuristic learning of high-order fuzzy-trend and jump rules

We propose a heuristic learning method forecasting future performance of stock market indices based on high-order fuzzy-trend jump rules generated from historical training data. Firstly, the training time series (TSs) are fuzzified by equal intervals referencing to the whole mean differences of historical training data. Then, it generates the groups of nth-order fuzzy logical relationships (FLRs). With the knowledge of the generated relationship groups, it summarizes the probability of the jumps of the nth-order "down", "equal" and "up" trend rules, respectively. Finally, it performs the forecasting based on the nth-order FLRs and the probabilities of their corresponding jump rules. To evaluate the outcome of the presented model with the performances of the others, we use the presented model to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) dataset. The outcomes show that the presented model outperforms the other models using single factor and point-wise one-step ahead forecasts. Moreover, it is easily to realize by software computing without artificial participation and can be extended to deal with multiple years of dataset. We use this model to predict Shanghai Stock Exchange Composite Index (SHSECI) as well to analyze its effectiveness and universality.