Abstract
Temporal Point Processes (TPPs) are useful for modeling event sequences which do not occur at regular time intervals. For example, TPPs can be used to model the occurrence of earthquakes, social media activity, financial transactions, etc. Owing to their flexible nature and applicability in several real-world scenarios, TPPs have gained wide attention from the research community. In literature, TPPs have mostly been used to predict the occurrence of the next event (time) with limited focus on the type/category of the event, termed as the marker. Further, limited focus has been given to model the inter-dependency of the event time and marker information for more accurate predictions. To this effect, this research proposes a novel Deviation-based Marked Temporal Point Process (DMTPP) algorithm focused on predicting the marker corresponding to the next event. Specifically, the deviation between the estimated and actual occurrence of the event is modeled for predicting the event marker. The DMTPP model is explicitly useful in scenarios where the marker information is not known immediately with the event occurrence, but is instead obtained after some time. DMTPP utilizes a Recurrent Neural Network (RNN) as its backbone for encoding the historical sequence pattern, and models the dependence between the marker and event time prediction. Experiments have been performed on three publicly available datasets for different tasks, where the proposed DMTPP model demonstrates state-of-the-art performance. For example, an accuracy of 91.76% is obtained on the MIMIC-II dataset, demonstrating an improvement of over 6% from the state-of-the-art model.
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
Similar content being viewed by others
References
Daley, D.J., Vere-Jones, D.: An Introduction to the Theory of Point Processes: Volume I: Elementary Theory and Methods. Springer, Heidelberg (2003)
Diggle, P.J.: Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. CRC Press, Boca Raton (2013)
Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of ACM KDD, pp. 1555–1564 (2016)
Grant, S., Betts, B.: Encouraging user behaviour with achievements: an empirical study. In: Proceedings of MSR, pp. 65–68 (2013)
Guo, R., Li, J., Liu, H.: Initiator: noise-contrastive estimation for marked temporal point process. In: Proceedings of IJCAI, pp. 2191–2197 (2018)
Hawkes, A.G.: Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1), 83–90 (1971)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Isham, V., Westcott, M.: A self-correcting point process. Stoch. Process. Appl. 8(3), 335–347 (1979)
Ji, Y., et al.: Temporal heterogeneous interaction graph embedding for next-item recommendation. In: Proceedings of ECML-PKDD (2020)
Kemp, A.: Poisson Processes. Wiley Online Library (1994)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Data Mining in Time Series Databases, pp. 1–21 (2004)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
McHugh, M.L.: The chi-square test of independence. Biochem. Med. 23(2), 143–149 (2013)
Mei, H., Eisner, J.: The neural hawkes process: A neurally self-modulating multivariate point process. arXiv preprint arXiv:1612.09328 (2016)
Pan, Z., Du, H., Ngiam, K.Y., Wang, F., Shum, P., Feng, M.: A self-correcting deep learning approach to predict acute conditions in critical care. arXiv preprint arXiv:1901.04364 (2019)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Proceedings of NeurIPS, pp. 8024–8035 (2019)
Rasmussen, J.G.: Temporal Point Processes: The Conditional Intensity Function. Lecture Notes, Jan (2011)
Shchur, O., Biloš, M., Günnemann, S.: Intensity-free learning of temporal point processes. arXiv preprint arXiv:1909.12127 (2019)
Türkmen, A.C., Wang, Y., Smola, A.J.: Fastpoint: scalable deep point processes. In: Proceedings of ECML-PKDD, pp. 465–480 (2019)
Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM TIST 10(4), 1–34 (2019)
Wang, Y., Liu, S., Shen, H., Gao, J., Cheng, X.: Marked temporal dynamics modeling based on recurrent neural network. In: Proceedings of PAKDD, pp. 786–798 (2017)
Wu, W., Yan, J., Yang, X., Zha, H.: Decoupled learning for factorial marked temporal point processes. In: Proceedings of ACM KDD, pp. 2516–2525 (2018)
Xiao, S., Yan, J., Yang, X., Zha, H., Chu, S.: Modeling the intensity function of point process via recurrent neural networks. In: Proceedings of AAAI, vol. 31 (2017)
Zhang, Q., Lipani, A., Kirnap, O., Yilmaz, E.: Self-attentive hawkes processes. arXiv preprint arXiv:1907.07561 (2019)
Zhang, Q., Lipani, A., Kirnap, O., Yilmaz, E.: Self-attentive hawkes process. In: Proceedings of ICML, pp. 11183–11193 (2020)
Zhao, L.: Event Prediction in Big Data Era: A Systematic Survey. arXiv preprint arXiv:2007.09815 (2020)
Zhao, Q., Erdogdu, M.A., He, H.Y., Rajaraman, A., Leskovec, J.: Seismic: a self-exciting point process model for predicting tweet popularity. In: Proceedings of KDD, pp. 1513–1522 (2015)
Zuo, S., Jiang, H., Li, Z., Zhao, T., Zha, H.: Transformer hawkes process. In: Proceedings of ICML, pp. 11692–11702 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Chauhan, A.V.S., Reddy, S., Singh, M., Singh, K., Bhowmik, T. (2021). Deviation-Based Marked Temporal Point Process for Marker Prediction. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12975. Springer, Cham. https://doi.org/10.1007/978-3-030-86486-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-86486-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86485-9
Online ISBN: 978-3-030-86486-6
eBook Packages: Computer ScienceComputer Science (R0)
