Misplaced Subsequences Repairing with Application to Multivariate Industrial Time Series Data

TL;DR

This paper introduces a novel approach for detecting and repairing inconsistent subsequences in multivariate industrial IoT time series data, improving data quality for IoT applications.

Contribution

It proposes a new method combining anomaly detection and precise repair algorithms specifically designed for multivariate IoT time series inconsistencies.

Findings

Outperforms existing methods in detecting inconsistencies

Effectively repairs anomalies in complex IIoT scenarios

Validated on real-world datasets with superior accuracy

Abstract

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.