Abstract
Most existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertain. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each piece of data with the data of theprevious day in a historical training time series to generate a new fluctuation trend time series (FTTS). Then, we fuzzify the FTTS into a fuzzy-fluctuation time series (FFTS) according to the up, equal, or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of the future is nonlinear, a particle swarm optimization (PSO) algorithm is employed to estimate the proportions for the lagged variables of the fuzzy AR (n) model. Finally, we use the acquired parameters to forecast future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or similarkinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) and DAX30 index to verify its effectiveness and universality.
Highlights
It is well known that historic time series imply the behavior rules of a given phenomenon and can be used to forecast the future of the same event [1]
Many researchers have developed time series models to predict the future of a complex system, e.g., regression analysis [2], the autoregressive moving average (ARIMA) model [3], the autoregressive conditional heteroscedasticity (ARCH)
The initial first-order ARMA fuzzy time series forecasting model was proposed by Egrioglu et al [42] based on the particle swarm optimization method
Summary
It is well known that historic time series imply the behavior rules of a given phenomenon and can be used to forecast the future of the same event [1]. A vast majority of FTS models are first-order and high-order fuzzy AR (autoregressive) models These models can be considered as an equivalent version of AR (n) based on fuzzy lagged variables of time series. Many studies employed the adaptive fuzzy inference system (ANFIS) method [38] for time series forecasting. The initial first-order ARMA fuzzy time series forecasting model was proposed by Egrioglu et al [42] based on the particle swarm optimization method. Kocak [43] developed a high-order ARMA fuzzy time series model based on artificial neural networks. We present a novel method to forecast the fluctuation of a stock market based on a high-order AR (n) fuzzy time series model and particle swarm optimization (PSO) arithmetic.
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