Abstract
Recently, a growing amount of time series data has been collected in cloud data centers, making anomaly detection for multivariate time series analysis increasingly necessary. However, extracting meaningful features from multivariate time series remains challenging due to the limited amount of labeled data and highly complex temporal correlations. Additionally, many unsupervised deep learning methods often result in a high false alarm rate. This study proposes a new unsupervised multivariate time series anomaly prediction model called the Predictive Wasserstein Generative Adversarial Network with Gradient Penalty (PW-GAN-GP). Our model adopts both Wasserstein Distance and Gradient Penalty, making the adversarial training more stable and helping the generator’s output to more closely resemble the real data. Moreover, a novel anomaly score function combining reconstruction, discrimination, and prediction errors is used to improve precision while maintaining recall. The experimental results on four public cloud computing datasets demonstrate that our proposed PW-GAN-GP outperforms the suboptimal baseline, with improvements of 22.11% and 13.47% in precision and F1 scores, respectively.
Similar content being viewed by others
Data availability
The authors confirm that the data (Server Machine Dataset, Pooled Server Metrics and NASA Dataset) supporting this study’s findings are publicly available at https://github.com/smallcowbaby/OmniAnomaly, https://github.com/eBay/RANSynCoders and https://github.com/khundman/telemanom.
References
Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv CSUR 54(2):1–38
Xin R, Chen P, Zhao Z (2023) CausalRCA: causal inference based precise fine-grained root cause localization for microservice applications. J Syst Softw 203:111724
Bashar MA, Nayak R (2020) TAnoGAN: time series anomaly detection with generative adversarial networks. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 1778–1785
Kim H, Shon T (2022) Industrial network-based behavioral anomaly detection in AI-enabled smart manufacturing. J Supercomput 78(11):13554–13563
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Statistics 1050:10
Jabbar A, Li X, Omar B (2021) A survey on generative adversarial networks: variants, applications, and training. ACM Comput Surv CSUR 54(8):1–49
Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International Conference on Machine Learning. PMLR, pp 214–223
Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of Wasserstein GANs. Adv Neural Inf Process Syst 30
Li D, Chen D, Jin B, Shi L, Goh J, Ng S-K (2019) MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: International Conference on Artificial Neural Networks. Springer, pp 703–716
Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459
Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: European Conference on Principles of Data Mining and Knowledge Discovery. Springer, pp 15–27
Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp 93–104
Schölkopf B, Williamson RC, Smola A, Shawe-Taylor J, Platt J (1999) Support vector method for novelty detection. Adv Neural Inf Process Syst 12
Schubert E, Sander J, Ester M, Kriegel HP, Xu X (2017) DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans Database Syst TODS 42(3):1–21
Liu FT, Ting KM, Zhou Z-H (2008) Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE, pp 413–422
Çavdar T, Ebrahimpour N, Kakız MT, Günay FB (2023) Decision-making for the anomalies in IIoTs based on 1D convolutional neural networks and Dempster–Shafer theory (DS-1DCNN). J Supercomput 79(2):1683–1704
Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T (2018) Detecting spacecraft anomalies using LSTMS and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 387–395
Park S, Jung S, Jung S, Rho S, Hwang E (2021) Sliding window-based LightGBM model for electric load forecasting using anomaly repair. J Supercomput 77:12857–12878
Su Y, Zhao Y, Niu C, Liu R, Sun W, Pei D (2019) Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2828–2837
Malhotra P, Vig L, Shroff G, Agarwal P et al (2015) Long short term memory networks for anomaly detection in time series. In: Proceedings, vol 89, pp 89–94
Kingma DP, Welling M (2013) Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114
Zong B, Song Q, Min MR, Cheng W, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations
Park D, Hoshi Y, Kemp CC (2018) A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robot Autom Lett 3(3):1544–1551
Audibert J, Michiardi P, Guyard F, Marti S, Zuluaga MA (2020) USAD: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 3395–3404
Chen P, Xia Y, Pang S, Li J (2015) A probabilistic model for performance analysis of cloud infrastructures. Concurr Comput Pract Exp 27(17):4784–4796
Song Y, Xin R, Chen P, Zhang R, Chen J, Zhao Z (2023) Identifying performance anomalies in fluctuating cloud environments: a robust correlative-GNN-based explainable approach. Futur Gener Comput Syst 145:77–86
Wen P, Yang Z, Wu L, Qi S, Chen J, Chen P (2022) A novel convolutional adversarial framework for multivariate time series anomaly detection and explanation in cloud environment. Appl Sci 12(20):10390
Chen P, Liu H, Xin R, Carval T, Zhao J, Xia Y, Zhao Z (2022) Effectively detecting operational anomalies in large-scale IoT data infrastructures by using a GAN-based predictive model. Comput J
Zhao H, Wang Y, Duan J, Huang C, Cao D, Tong Y, Xu B, Bai J, Tong J, Zhang Q (2020) Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM). IEEE, pp 841–850
Dos Santos C, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp 69–78
Abdulaal A, Liu Z, Lancewicki T (2021) Practical approach to asynchronous multivariate time series anomaly detection and localization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 2485–2494
Funding
This research was funded by the Science and Technology Program of Sichuan Province under Grant Nos. 2020JDRC0067, 2021JDR0222, and 2020YFG0326, and the Talent Program of Xihua University under Grant Nos. Z202047 and Z222001.
Author information
Authors and Affiliations
Contributions
SQ contributed to methodology, conceptualization, investigation, software, visualization, and writing—original draft. JC contributed to methodology, supervision, and writing—review and editing. PC contributed to methodology, investigation, and conceptualization. PW contributed to investigation and validation. XN contributed to investigation and methodology. LX contributed to methodology and conceptualization.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no potential conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Qi, S., Chen, J., Chen, P. et al. An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers. J Supercomput 80, 1268–1293 (2024). https://doi.org/10.1007/s11227-023-05534-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-023-05534-3