Abstract
The anomaly detection of univariate time series data is becoming increasingly important, as early detection of anomalies is valuable in practical applications. However, due to the continuous influx of data streams and the dynamic changes in data patterns, real-time anomaly detection still poses challenges. Algorithms such as SPOT, DSPOT, and FluxEV are efficient unsupervised anomaly detection algorithms for data streams, but their detection performance still needs to be improved when processing large-scale data streams. To address this, we propose the Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameter Estimation (RTAD-TP), a fast and effective unsupervised real-time anomaly detection algorithm. We calculate the residual between the current value and the predicted value using Exponential Moving Average (EMA), and apply extreme value distribution to determine the threshold for the residual. In addition, we use two-parameter estimation to improve the speed and accuracy of parameter estimation in automatic thresholding, addressing the limitations of SPOT, DSPOT, and FluxEV. Experimental results show that the RTAD-TP algorithm has better detection performance than the baseline algorithm.
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Fan, Q., Tang, Y., Ding, X., Huangfu, Q., Ding, P. (2023). RTAD-TP: Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameter Estimation. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_9
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DOI: https://doi.org/10.1007/978-3-031-40283-8_9
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