| Summary: | 碩士 === 淡江大學 === 管理科學學系碩士班 === 102 === New product sales forecasting is a crucial task to many innovative companies. Conventionally, the accuracy of sales forecasting is conditional on long-term, sufficient data of sales history. However, the sales information involved with newly launched products is very limited, unavailable or inaccessible.
Since virtually all sorts of products are seasonal, the main concept of this study is to propose a seasonal sales forecasting model in the setting of box office for U.S. motion picture market using online open access data, such as critics, comments and ratings, which not only exhibit original interests of different stakeholders in a specific movie but also are available at both pre- and post-released phases.
The results of this proposed nonlinear model with desired accuracy manifested that the influence influence of online word of mouth and seasonality significant on box office in the U.S market, contrary to those of the movie characteristics and competitions both very insubstantial.
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