相較於流行音樂演唱會，古典音樂演唱會或演奏會有特別的收聽群，而且其演唱會的銷奏也反映不同的票券銷售行為。比如，以大台北地區之古典音樂演唱會為例，一般而言，低票價之座位的銷售狀況會比其他票價之座位銷售較佳。因此，了解古典音樂演唱會的票券銷售特性對演唱會行銷及銷售策略，是極為重要的。本研究利用複迴歸分析及類神經網路企圖以演唱會之相關資訊如古典音樂類型，是否有贊助商等資訊來預測不同票價之票種的銷售狀況。透過台灣某電子票券商所提供的資料，本論文以2010年至2011年間之古典音樂演唱會銷售資料作為研究的標地。從預測模型的研究可發現，類神經網路較複迴歸分析可獲得較佳之票卷銷售結果。平均而言，其預測誤差約為0.103 RMSE，可視為10％之總體票券銷售之預測誤差。實驗的結果並顯示對高價位之票券預測模型可取得較佳之預測結果。

Classical music, one of music genre, has its own special audience with unique characteristics of the consumer behavior. For example, for Taipei metropolitan in Taiwan, the ticket sales of classical music concerns with lower price in fact is better than the sales of tickets with higher price. Therefore knowing the characteristics and purchasing pattern of classical music concerts is very crucial for the success of their concert ticket promotion. In this research, multivariate regression analysis and Artificial Neural Network (ANN) were used to study and predict the classical music ticket sales of low, medium, and high price category by using data features which might affect the ticketing sales such as type of classical music, whether the concert has sponsorship, and so on. The ticket sales data of classical music concert from 2010 to 2011 in Taiwan provided by an online ticketing company in Taiwan was used for investigation. The experimental result shows that the ANN model can generate the better prediction result comparing with the multivariate regression method. In average, ANN model can obtain 0.103 of RMSE which can be considered as 10% prediction error in terms of the percentage of ticket sold across three price category. This prediction result also show the percentage of total ticket sold of high price category can be forecasted with higher accuracy.

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