Anomaly Detection Method for Multivariate Time Series Data Based on BLTranAD
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5618-6_2
Reference20 articles.
1. Zeng, J., Zhang, L., Shi, G., Liu, T., Lin, K.: An ARIMA based real-time monitoring and warning algorithm for anomaly detection. In: 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS), pp. 469–476 (2017)
2. Noiboar, A., Cohen, I.: Anomaly detection based on wavelet domain GARCH random field modeling. IEEE Trans. Geosci. Remote Sens. 45(5), 1361–1373 (2007)
3. Zhang, C., Li, Y., Li, J., Li, G., Ma, H.: Multivariate time series anomaly detection method based on mTranAD. In: International Conference on Intelligent Computing, vol. 14089, pp. 52–63 (2023)
4. Pan, J.: Multivariate time series forecasting technologies and applications. East China Normal University (2023)
5. Paulauskas, N., Baskys, A.: Application of histogram-based outlier scores to detect computer network anomalies. Electronics 8(11), 1251 (2019)
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