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• W1547737260 abstract "Forecasting product demand is an important yet challenging planning tool for many industries. It is particularly challenging in industries that feature highly innovative products such as songs or movies in the entertainment industry. The challenge arises from the fact that, since products are highly innovative and thus very distinct, not many comparisons can be drawn from the success of previous products. We propose a novel approach for modeling the decay rate of a product’s demand using the predictive power of online virtual stock markets (VSMs). VSMs are online communities that, in a market-like fashion, gather the crowds’ opinion about a particular product. We use functional data analysis techniques to capture the pattern of pre-release VSM trading values and hence predict a product’s post-release revenue decay. Since both the predictor and response are functional, we first decompose the VSM and decay data using functional shape analysis, and then apply non-linear methods, such as boosting or neural networks, to predict their relationship. We show that this approach provides superior predictive accuracy in comparison to standard methods. Finally, we develop graphical methods to aid in understanding the relationship between a movie’s VSM history and its final box office revenue pattern. These plots show that different patterns in the VSM curves can result in very different box office returns." @default.
• W1547737260 created "2016-06-24" @default.
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• W1547737260 date "2008-01-01" @default.
• W1547737260 modified "2023-09-27" @default.
• W1547737260 title "Functional Forecasting of Demand Decay Rates using Online Virtual Stock Markets" @default.
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