2020/12/29 by Xiaoou Ding, Hongzhi Wang, Ding, Xiaoou +5
Computer Science · #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques #Databases (cs.DB) #FOS: Computer and information sciences #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2012.14555
openalex publication_date 2020/12/29 · openalex created_date 2021/01/05 · openalex updated_date 2026/07/28
Both the volume and the collection velocity of time series generated by monitoring sensors are increasing in the Internet of Things (IoT). Data management and analysis requires high quality and applicability of the IoT data. However, errors are prevalent in original time series data. Inconsistency in time series is a serious data quality problem existing widely in IoT. Such problem could be hardly solved by existing techniques. Motivated by this, we define an inconsistent subsequences problem in multivariate time series, and propose an integrity data repair approach to solve inconsistent problems. Our proposed repairing method consists of two parts: (1) we design effective anomaly detection method to discover latent inconsistent subsequences in the IoT time series; and (2) we develop repair algorithms to precisely locate the start and finish time of inconsistent intervals, and provide reliable repairing strategies. A thorough experiment on two real-life datasets verifies the superiority of our method compared to other practical approaches. Experimental results also show that our method captures and repairs inconsistency problems effectively in industrial time series in complex IIoT scenarios.