| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 96 === Although the task of time series is full of noise and non-stationary, the value of its proper exploitation has attracted both researchers and practitioners. This thesis uses tools from the field of artificial intelligence (AI) such as the support vector machine (SVM) and the back propagation neural network (BPN) in order to predict the non-stationary movement of time series. More specifically, three ensemble strategies, i.e. the median based selective ensemble, the time-lag based ensemble, and the time-lag based selective ensemble are used. These three ensembles are designed to deal with the three problems, i.e. the low accuracy predicted by a single classifier due to the noise of data, not enough training samples as only data samples located near to the target sample are useful, the time lag problem of the traditional moving average (MA) approach. The first ensemble strategy handles the first problem successfully. The second and third ensemble strategies overcome the other problems. According to the experimental results from 50 small categories of products of the C company, the proposed ensemble strategies are able to deal with such three problems and therefore improve the prediction performance evaluated by the mean absolute percentage error (MAPE).
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