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MULTI-SCALE MULTI-GRANULARITY SPATIAL-TEMPORAL TRAFFIC VOLUME PREDICTION

机译:多尺度多粒度空间交通量预测

摘要

Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.
机译:响应于网络流量的用于分配网络资源的方法和系统包括用于不同站点和区域的细空间粒度业务和粗空间粒度流量之间的空间相关性,以确定网络中的一个或多个站点的空间特征向量。基于空间特征向量,微空间粒度处的时间相关性在多个时间尺度上进行建模。基于空间特征向量,粗糙空间粒度处的时间相关性在多个时间尺度上进行建模。基于在粗糙空间粒度下的时间相关性和粗糙空间粒度处的时间相关性,确定网络中的一个或多个站点的业务流预测。根据业务流预测,在一个或多个站点提供网络资源。

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