论文信息 - Improving novelty detection in short time series through RBF-DDA parameter adjustment

Improving novelty detection in short time series through RBF-DDA parameter adjustment

Novelty detection in time series is an important problem with application in different domains. such as machine failure detection, fraud detection and auditing. We have previously proposed a method for time series novelty detection based on classification of time series windows by RBF-DDA neural networks. The paper proposes a method to be used in conjunction with this time series novelty detection method whose aim is to improve performance by adequately selecting the window size and the RBF-DDA parameter values. The method was evaluated on six real-world time series and the results obtained show that it greatly improves novelty detection performance.

A.L.I. Oliveira | F.B.L. Neto | S.R.L. Meira

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