Name
Abstract | ||
|---|---|---|
Network traffic forecasting during holidays is important for efficient congestion management and capacity planning. Unfortunately, there is not always enough relevant historical data to forecast traffic at base stations (BSes) individually - this is true in real systems where BSes may be newly built and the data acquisition centers are updated regularly. Hence, it is preferable to forecast holidays in groups of similar traffic patterns, so that data from different BSes can be gathered together to train a single model, and consistent prediction accuracy can be obtained when the context varies. This paper introduces a decomposed model consisting of trend, seasonality, and holiday components of traffic. Then, we focus on developing the holiday sub-models based on residuals that are not covered by trend and seasonality components. A modified k-means algorithm is proposed to cluster residual holiday data. We evaluate our hybrid holiday traffic prediction algorithm on real cellular network data and compare it with the open-source Prophet model developed by Facebook. The evaluation shows that the hybrid prediction method considerably boosts the performance of holiday predictions. |
Year | Venue | Field |
|---|---|---|
2018 | IEEE IFIP Network Operations and Management Symposium | Time series,Residual,Base station,Data mining,Data modeling,Computer science,Data acquisition,Capacity planning,Cellular network,Market research,Distributed computing |
DocType | ISSN | Citations |
Conference | 1542-1201 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Xu | 1 | 211 | 18.89 |
| Qiaoling Wang | 2 | 0 | 0.34 |
| Qinliang Lin | 3 | 0 | 0.34 |