Abstract
The rise of big data has brought various challenges and revolutions to many fields. Even though its development in many industries has gradually become perfect or even mature, its application and development in complex industrial scenarios is still in its infancy. We run research on single-dimensional time series point anomaly detection based on unsupervised learning: Unlike periodic time series, aperiodic or weakly periodic time series in industrial scenarios are more common. Considering the need for online real-time monitoring, we need to solve the problem of point anomaly detection of oil chromatographic characteristic gases. Thus, we propose a sliding window-based method for the unsupervised single-dimensional time series point anomaly detection problem called the confidence interval radius slope method (CIRS). CIRS is a fusion of knowledge-driven and data-driven methods to realize online real-time monitoring of possible data quality problems. From the experimental results, CIRS has obtained higher PR values than other unsupervised methods by the subject data.
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Acknowledgments
The project is supported by State Grid Research Project “Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).
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Lei, S. et al. (2022). CIRS: A Confidence Interval Radius Slope Method for Time Series Points Based on Unsupervised Learning. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_23
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DOI: https://doi.org/10.1007/978-981-19-5194-7_23
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