Name
Abstract | ||
|---|---|---|
The task of the daily box office prediction model is to build a dynamic prediction model to rolling forecast daily box office. It is a complex task as the movie box office has a short life cycle, and the static data and dynamic data that affect the trend of box office are heterogeneous. This paper proposes an end-to-end deep learning model for daily box office prediction, called Deep-DBP which consists of temporal component and static characteristics component. The temporal component is the main component which uses LSTM to learn the temporal dependencies between data points. The static characteristics component is an auxiliary component and it integrates static characteristics to improve prediction effect. The Deep-DBP can overcome the problems that the ARIMA and traditional ANN model cannot solve. The structure of input and output proposed in the model can well handle short time series prediction problem. It is a successful case in dealing with multi-source and multi-view data, addition of static characteristics component reduces the prediction error by 7%. The prediction error of Deep-DBP is 30.1%, which is better than that of the previous model. The experiment proved that the more training data collected, the better the prediction effect. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cogsys.2018.06.018 | Cognitive Systems Research |
Keywords | Field | DocType |
LSTM,Movie box office,Prediction,Time series,Deep neural network | Data point,Mean squared prediction error,Psychology,Input/output,Autoregressive integrated moving average,Dynamic data,Artificial intelligence,Dynamic prediction,Deep learning,Machine learning,Deep neural networks | Journal |
Volume | ISSN | Citations |
52 | 1389-0417 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunian Ru | 1 | 0 | 0.34 |
| Bo Li | 2 | 171 | 67.08 |
| Jianbo Liu | 3 | 7 | 2.91 |
| Jianping Chai | 4 | 1 | 5.10 |