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.

中文翻译：

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错位子序列修复及其在多元工业时间序列数据中的应用

在物联网（IoT）中，由监视传感器生成的时间序列的数量和收集速度都在增加。数据管理和分析需要物联网数据的高质量和适用性。但是，错误在原始时间序列数据中普遍存在。时间序列不一致是物联网中广泛存在的严重数据质量问题。现有技术很难解决这个问题。因此，我们在多元时间序列中定义了一个不一致的子序列问题，并提出了一种完整性数据修复方法来解决不一致的问题。我们提出的修复方法包括两个部分：（1）设计有效的异常检测方法，以发现物联网时间序列中潜在的不一致子序列；（2）开发维修算法，以精确定位不一致间隔的开始和结束时间，并提供可靠的维修策略。在两个实际数据集上进行的彻底实验证明了我们的方法与其他实际方法相比的优越性。实验结果还表明，在复杂的IIoT场景中，我们的方法可以有效地捕获和修复工业时间序列中的不一致问题。