An Intelligent Prediction Method for Short-term Time Serie..

1 Bao Rong Chang, Dept. of Electrical Engineering, Cheng Shiu Institute of Technology, Kaoshiung, Taiwan [email protected] 2 Shiou Fen Tsai, Dept. of International Trades, Kao Yuan Institute of Technology, Kaoshiung, Taiwan [email protected] Abstract  Traditional methods usually encounter the problem in which the predicted results cannot reach a satisfactory need because the overshooting prediction value made by the forecasting model cause a big residual error at the turning points where the peak or valley observed values occurred. Therefore, this study introduced a intelligent prediction algorithm (including two types) utilized for the applications of non-periodic short-term time series forecast. This algorithm actually is a hybrid model, combing a grey prediction model and a cumulative least squared linear prediction model, with the technique of automatically compensating a possible overestimated predicted value by a potential damped predicted value for those predicting points having extreme peak or valley value. The verification of this study is also tested successfully in three experiments whose are stock price index, economy growth rate forecasts, and typhoon moving trace. Furthermore, the results of this intelligent algorithm also concluded that the proposed one achieved the best accuracy of predicted values in these experiments when compared with other five traditional forecasting models discussed in the experiments.