Name
Abstract | ||
|---|---|---|
Mining and forecasting time-series incidents in large cities is very useful for the administration. However, most of the existing time-series prediction methods use univariate models which ignore the relationship among different city incidents. This paper proposes RBTA, a multivariate time-series model, to find the patterns including basic trend, seasonality, irregular components and relationship among different incidents. We evaluate our model on the real dataset from the downtown area of Shanghai, one the biggest metropolitan of the world. The average forecasting root mean squared error(RMSE) is 0.15, which decreases 4.9% comparing to the best one of the existing methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CBD.2017.66 | 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD) |
Keywords | Field | DocType |
Urban incident,Time-series,Forecast | Data mining,Multivariate statistics,Seasonality,Downtown,Mean squared error,Univariate,Statistics,Metropolitan area,Geography | Conference |
ISSN | ISBN | Citations |
2573-301X | 978-1-5386-1073-2 | 0 |
PageRank | References | Authors |
0.34 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jieyi Wang | 1 | 0 | 0.34 |
| Yongkun Wang | 2 | 20 | 4.78 |
| Yaohui Jin | 3 | 143 | 29.65 |