Local Recurrence Rates with Automatic Time Windows for Discord Search in Multivariate Time Series

Abstract

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 manufacturing machines, the issue of malfunction can be detected based on the evaluation of operating conditions. Recently, the method called Local Recurrence Rate based Discord Search (LRRDS) was proposed to convert time series data to Recurrence Plot (RP) for further discord investigation. Although LRRDS shows promising performance, it will need to pre-determine the window size for local search and might miss the discord if the small time series data is used for scanning. In this research, an improved computational framework for discord search under LRRDS is proposed. First, the time series data is transformed as a single image by utilizing the recurrence plot (RP)-based method and the Kullblack-Leibler Divergence is used to provide more accurate distance measures to its RP formulation. Second, the subsequence search is performed through automatic time window local search. The time window is adjusted to the characteristic of the element exists in the time window. Then, each corresponding subsequence could be compared with the other non-overlapping neighbor subsequences. Subsequences with the largest value of dissimilarity are considered the discord by the algorithm. The results show that the proposed approach can detect discords and works well under smaller data sizes without pre-determining the size of the windows.