  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.
