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文件名称:异常检测英文
文件大小:466KB
文件格式:PDF
更新时间:2017-01-30 03:50:58
异常检测 数据驱动
The deployment of environmental sensors has generated an interest in real-time applications of the data
they collect. This research develops a real-time anomaly detection method for environmental data
streams that can be used to identify data that deviate from historical patterns. The method is based on an
autoregressive data-driven model of the data stream and its corresponding prediction interval. It
performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data,
and requires no pre-classification of anomalies. Furthermore, this method can be easily deployed on
a large heterogeneous sensor network. Sixteen instantiations of this method are compared based on
their ability to identify measurement errors in a windspeed data stream from Corpus Christi, Texas. The
results indicate that a multilayer perceptron model of the data stream, coupled with replacement of
anomalous data points, performs well at identifying erroneous data in this data stream