Abstract
Addressing imbalanced multivariate time series classification remains challenging due to skewed class distribution, resulting in suboptimal minority class classification. High dimensionality and temporal dependencies further complicate the task. We propose a novel model with dynamic cost-sensitive weighting to handle this. Our model employs multi-head self-attention and a transformer structure to capture dependencies. The proposed dynamic cost-sensitive weighting function enhances imbalanced multivariate time series handling with anomalies across classes. We comprehensively evaluated our model using KPI-monitored multivariate time series data via a microservice benchmark, comparing against baselines. Results underscore our model’s efficacy, especially in cloud computing and deep learning contexts.
This research was supported by the Science and Technology Program of Sichuan Province under Grant No. 2020JDRC0067, No. 2023JDRC0087, and No. 2020YFG0326, and the Talent Program of Xihua University under Grant No. Z202047 and No. Z222001.
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Qi, S., Chen, J., Chen, P., Li, J., Shan, W., Wen, P. (2023). An Effective Dynamic Cost-Sensitive Weighting Based Anomaly Multi-classification Model for Imbalanced Multivariate Time Series. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_60
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DOI: https://doi.org/10.1007/978-981-99-7254-8_60
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