Feature Extraction in Abnormal Pattern Recognition of Financial Transaction

Feature extractors are used to get mathematical features that can be machine readable. In this paper we proposed a novel feature extraction and similarity measurement method based on RBF neural network one-step deviation prediction, which is different from traditional time series researches. The method converts time series similarity to feature vectors similarity comparison, while feature vectors are associated with physical information. Experiments show that this method has obvious advantages compared to traditional time series researches. It can detect abnormal patterns of financial transactions effectively.