Discord search in time series data plays an important role in analyzing data collected from the manufacturing site. By identifying anomalous subsequences of time series data collected from the manufacturing machines, the issue of malfunction based on the evaluation of operating conditions can be detected. Recently, a method called Local Recurrence Rate based Discord Search (LRRDS) was proposed to convert time series data into Recurrence Plot (RP) for further discord investigation. Although LRRDS shows promising performance, it could be further improved. Moreover, its capability in the streaming environment also has not been tested. In this research, an improved computational framework for discord search under LRRDS is proposed. First, the time series data is transformed into an RP and the Kullback-Leibler divergence is used to provide more accurate distance measure to its RP formulation. Second, the subsequence characteristic measurement is improved by adding integral as a statistical feature. Then, each corresponding subsequence can be compared with other non-overlapping neighbor subsequences. Subsequence with the largest value of dissimilarity is considered as a discord by the algorithm. The capability of the improved LRRDS and the original LRRDS were compared on various multivariate time series datasets, and also in a simulated streaming environment. The results show that the proposed approach can detect discords and it works fine under a simulated streaming environment.

Discord search in time series data plays an important role in analyzing data collected from the manufacturing site. By identifying anomalous subsequences of time series data collected from the manufacturing machines, the issue of malfunction based on the evaluation of operating conditions can be detected. Recently, a method called Local Recurrence Rate based Discord Search (LRRDS) was proposed to convert time series data into Recurrence Plot (RP) for further discord investigation. Although LRRDS shows promising performance, it could be further improved. Moreover, its capability in the streaming environment also has not been tested. In this research, an improved computational framework for discord search under LRRDS is proposed. First, the time series data is transformed into an RP and the Kullback-Leibler divergence is used to provide more accurate distance measure to its RP formulation. Second, the subsequence characteristic measurement is improved by adding integral as a statistical feature. Then, each corresponding subsequence can be compared with other non-overlapping neighbor subsequences. Subsequence with the largest value of dissimilarity is considered as a discord by the algorithm. The capability of the improved LRRDS and the original LRRDS were compared on various multivariate time series datasets, and also in a simulated streaming environment. The results show that the proposed approach can detect discords and it works fine under a simulated streaming environment.

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