Name
Abstract | ||
|---|---|---|
In this paper, we propose a robust piecewise parametric model for predicting the cumulative number of participants in large-scale events. Based on the analysis of arriving patterns in such events, we establish parametric models for different periods and design an efficient fitting strategy to achieve model parameters from incomplete current data. Moreover, based on historical data, we can train parameters by neural network and get relation prior among parameters and data. With the help of relation prior, we can update the parameters of current data and achieve robust prediction for outlier. Simulation results on the database of Expo 2010 Shanghai show the good performance of our proposed method even in abnormal situations. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34595-1_53 | ADVANCES ON DIGITAL TELEVISION AND WIRELESS MULTIMEDIA COMMUNICATIONS |
Keywords | Field | DocType |
large-scale event,robust prediction,piecewise parametric model,relation prior | Data mining,Parametric model,Pattern recognition,Computer science,Outlier,Artificial intelligence,Artificial neural network,Piecewise | Conference |
Volume | ISSN | Citations |
331 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dixin Luo | 1 | 37 | 6.98 |
| Rong Xie | 2 | 59 | 34.58 |
| Jia Wang | 3 | 79 | 17.75 |
| Wenjun Zhang | 4 | 1789 | 177.28 |