A High-Precision Approach of Traffic Anomaly Based on Fractal and Binary Symbolic

In this paper it proposed a high-precision approach based on fractal and binary symbol which can be used into traffic anomaly. Dozens of high level representations of time series have been introduced for data mining in the literature. Traditional dimension reduction methods about traffic anomaly introduce the smoothness to data series in some degree that the important features of time series about non-linearity and fractal are destroyed. But they have a common question if they are used to be in the traffic area. That is the speed that carries on data mining too slowly. The traffic anomaly approach is unique in which it allows dimensionality reduction and it also preserved the fractal features. The experiments have been performed on synthetic, as well as traffic data sequences to evaluate the proposed method.