Abstract
Multivariate time series anomaly detection is a crucial area of research in several domains, including finance, logistics, and manufacturing. Successfully identifying abnormal behaviors or events can help prevent disruptions, but the high false positive rate in this field is a significant challenge that affects detection accuracy. In this paper, we propose a novel method, mTranAD, which improves upon the TranAD algorithm by leveraging the benefits of Transformer and variational autoencoder (VAE) in multivariate unsupervised anomaly detection. Specifically, mTranAD replaces TranAD’s autoencoder structure with a VAE and trains it using the VAE’s loss function. The incorporation of latent variables in the VAE model enables accurate reconstruction of data and mapping of data to a lower dimensional latent space, allowing for a more efficient description of input data complexity with fewer parameters. By utilizing these latent variables, the model can effectively handle high-dimensional, complex data and exhibit greater flexibility when generating new data. We conduct experiments on four public datasets (NAB, MBA, SMAP and WADI) and compare mTranAD’s performance with 11 other state-of-the-art methods, including TranAD, MERLIN, LSTM-NDT, OmniAnomaly, USAD, and DAGMM. The experimental results demonstrate that mTranAD outperforms these methods in terms of performance, accuracy, and reliability. The primary purpose of this paper is to improve the TranAD algorithm and enhance the accuracy of multivariate time series anomaly detection by reducing the false positive rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tuli, S.: TranAD: deep transformer networks for anomaly detection in multivariate time series data. VLDB (2022)
Léon, M.: Towards principled methods for training generative adversarial networks. In 5th International Conference on Learning Representations, ICLR (2017)
Yang, Q.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
Osada, G., Omote, K., Nishide, T.: Network intrusion detection based on semi-supervised variational auto-encoder. In: Foley, S.N., Gollmann, D., Snekkenes, E. (eds.) ESORICS 2017. LNCS, vol. 10493, pp. 344–361. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66399-9_19
Vaswani, A.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000–6010 (2017)
Wang, Y., Masoud, N.: Real-time sensor anomaly detection and recovery in connected automated vehicle sensors. IEEE Trans. Intell. Trans. Syst. 22(3), 1411–1421 (2021)
Tuli, S., Casale, G.: PreGAN: preemptive migration prediction network for proactive fault-tolerant edge computing. In: IEEE Conference on Computer Communications (INFOCOM), pp. 670–679. IEEE (2022)
An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability (2015)
Rani, B.J.B.: Survey on applying GAN for anomaly detection. In: 2020 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1–5 (2020)
Chen, J., Pi, D: Imbalanced satellite telemetry data anomaly detection model based on Bayesian LSTM. Acta Astronautica 80, 232–242, ISSN 0094–5765 (2021)
Rousseeuw, P., Perrotta, D.: Robust monitoring of time series with application to fraud detection. Econometrics Stat. 9, 108–121, ISSN 2452–3062 (2019)
Ding, M., Tian, H: PCA-based network traffic anomaly detection. Tsinghua Sci. Technol. 21(5), 500–509 (2016)
Hu, M.: A novel computational approach for discord search with local recurrence rates in multivariate time series. Inf. Sci. 477, pp. 220–233, ISSN 0020–0255 (2018)
Abbasimehr, H.: An optimized model using LSTM network for demand forecasting. Comput. Ind. Eng. 143, 106435 (2020)
Audibert, J., Michiardi, P.: 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 (2020)
Li, D.: MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In: International Conference on Artificial Neural Networks, pp. 703–716 . Springer (2019). https://doi.org/10.1007/978-3-030-30490-4_56
Zhao, H., Wang, Y.: Multivariate time-series anomaly detection via graph attention network. In: International Conference on Data Mining, pp. 841–850 (2020)
Zhang, Y.: Unsupervised deep anomaly detection for multi-sensor time-series signals. IEEE Trans. Knowl. Data Eng. (2021)
Deng, A.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4027–4035 (2021)
Nakamura, T., Imamura, M.: MERLIN: parameter-free discovery of arbitrary length anomalies in massive time series archives. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1190–1195. IEEE (2020)
Zong, B., Song, Q.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
Hundman, K.: 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 (2018)
Huang, S.: Hit anomaly: hierarchical transformers for anomaly detection in system log. IEEE Trans. Netw. Serv. Manage. 17(4), 2064–2076 (2020)
Cook, A.A: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481–6494 (2020)
Ahmad, S.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262, 134–147 (2017)
Dai, E., Chen, J.: Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series. United States. ICLR (2022)
Shukla, S.N.: Heteroscedastic temporal variational autoencoder for irregularly sampled time series. ICLR (2021)
Tang, W., Long, G.: Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification. In: International Conference on Learning Representations, ICLR (2022)
Shin, Y., Yoon, S.: Coherence-based label propagation over time series for accelerated active learning. In: International Conference on Learning Representation, ICLR (2022)
Kieu, T.: Outlier detection for multidimensional time series using deep neural networks. In: 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark, pp. 125–134 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, C., Li, Y., Li, J., Li, G., Ma, H. (2023). Multivariate Time Series Anomaly Detection Method Based on mTranAD. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-4752-2_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4751-5
Online ISBN: 978-981-99-4752-2
eBook Packages: Computer ScienceComputer Science (R0)