A novel computational approach for discord search with local recurrence rates in multivariate time series

Discord search is an important technique for time series analysis, especially for anomaly detection. In recent years, many computational approaches of discord search were studied; however, limitation exists while only the problems with univariate time series data can be well addressed. In this study, we proposed a novel computational framework to identify discords from multivariate time series (MTS) data, namely, LRRDS (Local Recurrence Rate based Discord Search). LRRDS accurately identifies the discords by analyzing a recurrence plot, which is transformed from the original time series data. An innovative strategy was employed to improve the efficiency for pair-wise distance comparison of two subsequences. In the experimental simulations, LRRDS was applied to an extensive number of MTS datasets. Results show that the proposed approach is more efficient than existing methods, such as GDS. In conclusion, the LRRDS approach solves the adaptability problem of discord sequences in multi-dimensional space and guarantees the computational effectiveness and efficiency. (C) 2018 The Authors. Published by Elsevier Inc.