Proposal of a Time Series Anomaly Detection Method Using Image Encoding Techniques

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Ryo Sakurai Takehisa Yairi

Abstract

Time series anomaly detection is considered to play a major role in many areas of society. Models using RNN, which are well suited for time series data, have been studied. However, models using RNN have the problem of high cost; image en- coding approaches combining Gramian Angular Fields(GAF) and Autoencoder are less expensive than RNN. However, the accuracy in existing studies is not as good as RNN. In this paper, we propose a time-series anomaly detection frame- work that first focuses on the structural issues of GAF and the reconstruction accuracy of Autoencoder. Experiments were conducted to verify the effectiveness of the framework. The results showed that the approach focusing on the structural is- sues of GAF achieved a significant improvement in accuracy, while the approach focusing on improving the reconstruction accuracy of the Autoencoder network decreased the anomaly detection accuracy. The reason for the lower accuracy was found to be that the networks with higher reconstruction accuracy accurately reconstructed even the anomaly images, making anomaly detection based on L1 errors impossible. These results indicate that an approach that focuses on the structural problems of GAF is effective, while an approach that improves the reconstruction accuracy of Autoencoder is not necessarily effective.

Abstract 193 | PDF Downloads 265

##plugins.themes.bootstrap3.article.details##

Keywords

Deep learning, AutoEncoder(AE), Time series analysis, Anomaly detection, Gramian Angular Fields(GAF)

References
Bergmann, P., Lowe, S., Fauser, M., Sattlegger, D., & Steger, C. (2019). Improving unsupervised defect seg- mentation by applying structural similarity to autoen- coders. In Proceedings of the 14th international joint conference on computer vision, imaging and computer graphics theory and applications. SCITEPRESS - Science and Technology Publications. doi: 10.5220/ 0007364503720380

Garcia, G. R., Michau, G., Ducoffe, M., Gupta, J. S., & Fink, O. (2021, feb). Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(4), 617–627. doi: 10.1177/1748006x21994446

Graves, A. (Ed.). (2012). Supervised sequence labelling with recurrent neural networks. Springer Berlin Heidelberg.

Hinton, G. E., & Salakhutdinov, R. R. (2006, July). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. doi: 10.1126/science .1127647

Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T. (2018, jul). Detecting space- craft anomalies using LSTMs and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. ACM. doi: 10.1145/ 3219819.3219845

Mogren, O. (2016). C-rnn-gan: Continuous recurrent neural networks with adversarial training. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmen- tation.

Wang, Z., & Oates, T. (2014). Encoding time series as images for visual inspection and classification using tiled convolutional neural networks..
Section
Special Session Papers