Ticket Sales Prediction of Entertainment Show Using Functional Data Clustering and Artificial Neural Network

• Main Authors: Chih-Huang Huang, 黃智煌
• Other Authors: Chao-Lung Yang
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/w8w9u5

碩士 === 國立臺灣科技大學 === 工業管理系 === 104 === 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.