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

Abstract

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.