演唱會票卷的銷售狀態，反應演唱會的熱門程度與受歡迎程度，也關係著售票廠商的營收與利潤，因此售票廠商透過各種促銷與宣傳均希望使演唱會票卷能夠銷售完畢，以期達到最大收益。但售票廠商若想得知該場演唱會的票卷是否能夠販售完畢，除了靠過往經驗主觀判斷是否能夠有其他方法能夠提供更為科學與精確的預測?為了瞭解票卷系統背後的銷售情形，本研究以某售票系統的票卷銷售資料做為研究基礎，使用函數型資料分析與群集分析，並透過類神經網路模型的應用，進行銷售預測模型的建構。首先，將各演唱會之票價進行資料的劃分，使演唱會票卷的銷售預測能依據不同的票價給予不同的預測分析結果。接著使用函數型資料分析方法將票卷銷售資料進行函數化，使原本離散間斷的資料轉換為平滑之函數型資料，並利用B-spline 為轉換基底，以函數資料代表各演唱會之銷售狀態。本研究提出以函數型分群演算法將具有相同銷售狀態的演唱會場次進行群集分析，最後將具有相同特徵之演唱會，透過類神經網絡模型進行銷售預測值計算(Artificial Neural Network with Functional Data Clustering, ANN_FDC)，利用前期銷售時間點之銷售資料以預測在後期銷售時間點之票券銷售狀況，期望透過演唱會銷售資料的分析，瞭解該演唱會是否能夠達到完售狀態，以及距離完售還有多少百分比的差距。本研究以2010 至2011 年間於台北地區經常舉辦流行音樂演唱會之展演場地所舉辦演唱會之票券銷售記錄進行研究的實證，從實驗結果可發現本研究所提出之ANN_FDC 方法在預測結果及運算效能上比傳統ANN 有較佳的結果。本研究期望票券的銷售預測能有助於售票廠商於銷售前期得知銷售狀態之預測，以進行銷售策略的修訂與促銷活動投入的參考與依據。

The sales performance of an entertainment show or concert tickets not only reflect profit of the business but also represents the popularity of the event. Predicting or forecasting the ticket sales performance before or during the ticket on sale is very important for the organization which hosts entertainment show event. In this research, a ticket sales prediction model was developed to predict the percentage of box office (ticket sales) of each price ranges based on the historical sales performance. In this research, a method called “Artificial Neural Network with Functional Data Clustering” (ANN_FDC) was proposed. Basically, the functional data clustering method is utilized to cluster show events by their ticket sale trajectory. Based on the clustering result, Artificial Neural Network (ANN) was developed for each ticket price range to predict the sales of the box office in terms of the percentage of ticket sold. This method is applied by using the first half of sale records to train the model for predicting the box office in the second half of sale periods. In this research, the 2010~2011 ticket sales data of Taipei area which usually exhibit pop music concerts was used as the testbed for evaluating the prediction model. The experimental results show the ANN_FDC can provide the better prediction and computational efficiency. This result can be further used for the study of the ticket marketing and sales promotion strategies.

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