XUE Hai-dong; ZHU Qun-xiong
Based on the thoughts of structured analogy forecasting, a novel algorithm is proposed to solve the numeric time series probability prediction problem named Structured Analogy Prediction for Time Series(SAP-TS). SAP-TS constructs the conditional probability distribution through analogies, which avoids the obstacles encountered by classical probability methods, either weak predictability or intractable extremely large contingency tables, which also incurs lack of data problem. Furthermore, SAP-TS offers integrated confidence index to evaluate the prediction accuracy instantaneously. When applying SAP-TS to predict the acetic acid amount of Purified Terephthalic Acid(PTA) solvent system, the prediction results are more precise than the results of Generalized Regression Neural Network(GRNN). The previous best method, and the integrated confidence index also effectively evaluate the prediction accuracy.