A cosine similarity-based negative selection algorithm for time series novelty detection

Detecting the new or anomalous signal sequences in the observed time series data is a problem of great practical interest for many applications. The bio-inspired negative selection algorithm, whose main idea is to discriminate the non-self pattern from self pattern, has drawn much attention because only normal information is needed for training. Most of the proposed algorithms are based on binary-valued string matching. A real-valued negative selection algorithm for novelty detection in vibration signal is implemented in this paper. The vector set for calculation is constructed by sampling the discrete time series from a moving time window. The matching affinity between two vectors is measured by cosine similarity. The calculated results show that the cosine similarity-based algorithm is more practical for potential applications in online signal monitoring.