One Approach to Volatile Time Series Forecasting

Abstract

AbstractAn algorithm to form a universe covering weakly structured historical data of the time series and trapezoidal membership functions used fuzzification method are proposed. According to the obtained results, the proposed approach provides more appropriate (adequate) 1st-order fuzzy model for arbitrary volatile time series. Nevertheless, this model does not pretend to have absolute predicting accuracy, which can be achieved and/or achieved by other more complex higher-order models. The purpose of the paper is to show that the use of simple fuzzy prediction models of the 1st-order reserves the opportunity for further improvement of the predicting technology of weakly structured time series. Prediction results of the arbitrary time series demonstrate that the combination of universe establishment algorithms and historical data fuzzification using trapezoidal membership functions, the construction of 1st-order internal cause-effect relations and the method of defuzzification of outputs of the used fuzzy model can still be superior in quality predicting and prediction reliability not only similar 1st-order models, but other models of higher order as well.KeywordsVolatile time seriesWeakly structured dataFuzzy setMembership functionFuzzy modelTime series forecasting