We have developed a new framework using time-series analysis for dynamically
assigning mobile network traffic prediction models in previously unseen
wireless environments. Our framework selectively employs learned behaviors,
outperforming any single model with over a 50% improvement relative to current
studies. More importantly, it surpasses traditional approaches without needing
prior knowledge of a cell. While this paper focuses on network traffic
prediction using our adaptive forecasting framework, this framework can also be
applied to other machine learning applications in uncertain environments.
The framework begins with unsupervised clustering of time-series data to
identify unique trends and seasonal patterns. Subsequently, we apply supervised
learning for traffic volume prediction within each cluster. This specialization
towards specific traffic behaviors occurs without penalties from spatial and
temporal variations. Finally, the framework adaptively assigns trained models
to new, previously unseen cells. By analyzing real-time measurements of a cell,
our framework intelligently selects the most suitable cluster for that cell at
any given time, with cluster assignment dynamically adjusting to
spatio-temporal fluctuations.

利用时间序列分析为动态分配移动网络流量预测模型的新框架，通过选择性地使用学习行为相对于当前研究具有超过 50％的性能改进，并在无需对单个细胞具有先验知识的情况下超过传统方法；该框架通过无监督聚类识别独特趋势和季节模式，然后在每个聚类中应用监督学习进行流量量预测，使得不受空间和时间变化的惩罚，并且通过分析细胞的实时测量结果，智能选择最适合该细胞的聚类，使聚类分配根据时空波动的动态调整。