Abstract
Key points detection is crucial for signal analysis by marking the identification points of specific events. Deep learning methods have been introduced into key points detection tasks due to their significant representation learning ability. However, in contrast to common time series classification and prediction tasks, the target key points correspond to significantly different time-series patterns and account for an extremely small proportion in a whole sample. Consequently, existing end-to-end methods for key points detection encounter two major problems: specificity and sparsity. Thus, in this work, we address these issues by proposing a probability compensated self-supervised learning framework named ProCSS. Our ProCSS consists of two major components: 1) a pretext task module pretraining an encoder based on self-supervised learning to capture effective time-series representations with a higher generalization ability; 2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival methods for time-series key points detection.
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References
Aditi, B., Sanjay, K.: A robust approach to denoise ECG signals based on fractional Stockwell transform. Biomed. Signal Process. Control 62, 102090 (2020)
Ahad, N., Davenport, M.A.: Semi-supervised sequence classification through change point detection. CoRR abs/2009.11829 arXiv:2009.11829 (2020)
Allen, R.V.: Automatic earthquake recognition and timing from single traces. Bull. Seismol. Soc. Am. 68(5), 1521–1532 (1978)
Alom, Z., Hasan, M., Yakopcic, C.: Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. CoRR abs/1802.06955 (2018)
Baek, K., Lee, M., Shim, H.: PsyNet: self-supervised approach to object localization using point symmetric transformation. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 10451–10459 (2020)
Cauteruccio, F., et al.: A framework for anomaly detection and classification in multiple IoT scenarios. Futur. Gener. Comput. Syst. 114, 322–335 (2021)
Chen, J., Liu, Y., Carey, S.J., Dudek, P.: Proximity estimation using vision features computed on sensor. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2689–2695 (2020)
Chen, Y.: Automatic microseismic event picking via unsupervised machine learning. Geophys. J. Int. 222(3), 1750–1764 (2020)
Ding, D., Zhang, M., Pan, X., Yang, M., He, X.: Modeling extreme events in time series prediction. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1114–1122 (2019)
Dwass, M.: The asymptotic theory of extreme order statistics (janos galambos). SIAM Rev. 22(3), 379 (1980). https://doi.org/10.1137/1022076
Fisher, R.A., Tippett, L.H.C.: Limiting forms of the frequency distribution of the largest or smallest member of a sample. Math. Proc. Camb. Philos. Soc. 24(2), 180–190 (1928)
Gong, Y., Lai, C.J., Chung, Y., Glass, J.R.: SSAST: self-supervised audio spectrogram transformer. CoRR abs/2110.09784 arXiv:2110.09784 (2021)
Guizilini, V., Ramos, F.: Online self-supervised segmentation of dynamic objects. In: 2013 IEEE International Conference on Robotics and Automation, pp. 4720–4727 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
He, Z., Peng, P., Wang, L., Jiang, Y.: Pickcapsnet: capsule network for automatic p-wave arrival picking. IEEE Geosci. Remote Sens. Lett. 18(4), 617–621 (2021)
Huang, H., Lin, L., Tong, R., Hu, H.: UNet 3+: a full-scale connected UNet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059 (2020)
Ince, T., Kiranyaz, S., Eren, L., Askar, M., Gabbouj, M.: Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans. Ind. Electron. 63(11), 7067–7075 (2016)
Jenni, S., Favaro, P.: Self-supervised feature learning by learning to spot artifacts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Laitala, J., et al.: Robust ECG R-peak detection using LSTM. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 1104–1111 (2020)
Lee, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Unsupervised representation learning by sorting sequences. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 667–676 (2017)
Ma, H., Wang, T., Li, Y., Meng, Y.: A time picking method for microseismic data based on LLE and improved PSO clustering algorithm. IEEE Geosci. Remote Sens. Lett. 15(11), 1677–1681 (2018)
Ma, K., Leung, H.: A novel lstm approach for asynchronous multivariate time series prediction. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2019)
Ma, Q., Zheng, Z., Zheng, J., Li, S., Zhuang, W., Cottrell, G.W.: Joint-label learning by dual augmentation for time series classification. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, pp. 8847–8855. AAAI Press (2021)
Meng, Y., Li, Y., Zhang, C., Zhao, H.: A time picking method based on spectral multimanifold clustering in microseismic data. IEEE Geosci. Remote Sens. Lett. 14(8), 1273–1277 (2017)
Ozan, O., Jo, S., Loïc, L.: Attention u-net: learning where to look for the pancreas. CoRR abs/1804.03999 (2018)
Qin, X., et al.: Street-level air quality inference based on geographically context-aware random forest using opportunistic mobile sensor network. In: 2021 the 5th International Conference on Innovation in Artificial Intelligence, pp. 221–227 (2021)
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, pp. 759–766 (2007)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ross, Z.: Generalized seismic phase detection with deep learning. Bull. Seismol. Soc. Am. 108(5A), 2894–2901 (2018)
Sterzentsenko, V., et al.: Self-supervised deep depth denoising. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1242–1251 (2019)
Wang, J., Teng, T.: Artificial neural network-based seismic detector. Bull. Seismol. Soc. Am. 85(1), 308–319 (1995)
Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2794–2802 (2015)
Xue, F., Yan, W.: Multivariate time series anomaly detection with few positive samples. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2022)
Zahid, M.U., et al.: Robust r-peak detection in low-quality Holter ECGs using 1D convolutional neural network. IEEE Trans. Biomed. Eng. 69(1), 119–128 (2022)
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: Proceedings of the 38th International Conference on Machine Learning, pp. 12310–12320 (2021)
Zeng, J., Xie, P.: Contrastive self-supervised learning for graph classification. CoRR abs/2009.05923 arXiv:2009.05923 (2020)
Zhang, C., Peng, Y.: Better and faster: knowledge transfer from multiple self-supervised learning tasks via graph distillation for video classification. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 1135–1141 (2018)
Zhao, Y., Shang, Z., Lian, Y.: A 13.34 \({\mu }\)w event-driven patient-specific ANN cardiac arrhythmia classifier for wearable ECG sensors. IEEE Trans. Biomed. Circuits Syst. 14(2), 186–197 (2020)
Zheng, J.: An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks. Geophys. J. Int. 212(2), 1389–1397 (2017)
Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020)
Zhu, W., Beroza, G.: PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophys. J. Int. 216(1), 261–273 (2018)
Zhu, X., Chen, B., Wang, X., Li, T.: Time series segmentation clustering: a new method for s-phase picking in microseismic data. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
Acknowledgments
The work is supported by National Key R &D Program of China (Grant No. 2022YFB3304302), the National Natural Science Foundation of China (Grant No. 62072087, 61972077, 52109116), LiaoNing Revitalization Talents Program (Grant No. XLYC2007079).
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Yuan, M. et al. (2023). Towards Time-Series Key Points Detection Through Self-supervised Learning and Probability Compensation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_16
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