| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 96 === Generally speaking, there are two groups of methods in the field of forecasting, i.e., traditional statistic analysis and artificial intelligence techniques. The first includes the regression analysis, correlation analysis, discriminate analysis, logit model, probit model, etc. The second includes decision tree, neural network, support vector machine, gray prediction, fuzzy theory, etc. Such methods may need different attributes for their specific application. Therefore, in literature, most forecasting tasks focus on specific attributes in their specific problem domains. In particular, they only use one or two forecasting methods to compare with one or two other methods after relevant attributes have been determined. The scale of such comparison is not wide enough. Thus, this thesis firstly uses multiple forecasting tools in artificial intelligence field, such as support vector machine (SVM) and neural network (NN). Secondly this thesis uses traditional statistical forecasting methods, such as simple linear regression (SLR), moving average (MA), ordinary least squares (OLS), and isotonic regression. Thirdly this thesis uses hybrid approaches by integrating artificial intelligence approaches with traditional statistical forecasting approaches. Finally, we also combine bagging and stacking ensemble learning techniques to get an improvement for our problem domain in the thesis. The standard of evaluation used in the thesis is the mean absolute percentage error (MAPE) that evaluates the most suitable forecaster from those (i.e., 16) forecasters in our specific problem domain which may provide more complete concept when similar forecasting task is performed in the future. The results in the thesis show OLS is the best model in the traditional statistical group, the NN forecaster is the best model in the artificial intelligence group, the SLR bagging forecaster is the best model in bagging group and the method which integrates NN bagging with stacking model is the best one in sixteen model.
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