{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:02:24Z","timestamp":1771074144901,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","funder":[{"name":"Natural Science Foundation of Liaoning Province","award":["2025-BSLH-316"],"award-info":[{"award-number":["2025-BSLH-316"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,19]]},"DOI":"10.1145\/3788731.3788752","type":"proceedings-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:49:28Z","timestamp":1771069768000},"page":"128-141","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Feature Fusion Driven Method for Time Series Pre-event Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1336-2428","authenticated-orcid":false,"given":"Hanlin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Science, Shenyang University of Technology, Shenyang, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5966-335X","authenticated-orcid":false,"given":"Junlu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of information institute, Liaoning University, Shenyang, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4285-578X","authenticated-orcid":false,"given":"Baoyan","family":"Song","sequence":"additional","affiliation":[{"name":"School of information institute, Liaoning University, Shenyang, Liaoning, China"}]}],"member":"320","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"G. Atluri A. Karpatne and V. Kumar. 2018. Spatio-Temporal Data Mining: A Survey of Problems and Methods. ACM Computing Surveys (CSUR) 51 4 (2018) 1\u201341.","DOI":"10.1145\/3161602"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Brijesh\u00a0K Bansal Ajeet\u00a0P Pandey Ajay\u00a0P Singh Gaddale Suresh Ravi\u00a0K Singh and Jia\u00a0L Gautam. 2021. National seismological network in India for real-time earthquake monitoring. Seismological Society of America 92 4 (2021) 2255\u20132269.","DOI":"10.1785\/0220200327"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Guangzhao Chen Jingming Hou Haoqiang Dong Xia Zhou Yuan Hu XinXin Pan and Jiahao Lv. 2025. Ensemble just-in-time learning model driven by multi-similarity measurement methods for urban flood rapid prediction. Journal of Hydrology 661 (2025) 133732.","DOI":"10.1016\/j.jhydrol.2025.133732"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Z. Elamrani Abou\u00a0Elassad H. Mousannif and H. Al\u00a0Moatassime. 2020. Class-Imbalanced Crash Prediction Based on Real-Time Traffic and Weather Data: A Driving Simulator Study. Traffic Injury Prevention 21 3 (2020) 201\u2013208.","DOI":"10.1080\/15389588.2020.1723794"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Chenchen Fan Jingming Hou Xuan Li Gangfu Song Yihui Yang Xin Liang Qingshi Zhou Muhammad Imran Guangzhao Chen Ziyi Wang et\u00a0al. 2025. Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm. Water Research (2025) 123711.","DOI":"10.1016\/j.watres.2025.123711"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20317"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Gui Hu Linlin Li Zhiyuan Ren and Kan Zhang. 2023. The characteristics of the 2022 Tonga volcanic tsunami in the Pacific Ocean. Natural hazards and earth system sciences 23 2 (2023) 675\u2013691.","DOI":"10.5194\/nhess-23-675-2023"},{"key":"e_1_3_3_1_9_2","volume-title":"Forecasting: Principles and Practice (2nd ed.)","author":"Hyndman R.\u00a0J.","year":"2018","unstructured":"R.\u00a0J. Hyndman and G. Athanasopoulos. 2018. Forecasting: Principles and Practice (2nd ed.). OTexts, Australia. 438\u2013442 pages."},{"key":"e_1_3_3_1_10_2","unstructured":"M.\u00a0I.\u00a0K. Islam K.\u00a0M. Saifuddin T. Hossain et\u00a0al. 2024. DyGCL: Dynamic Graph Contrastive Learning for Event Prediction. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2404.15612 (2024)."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25555"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"G. Jin C. Liu Z. Xi et\u00a0al. 2022. Adaptive Dual-View WaveNet for Urban Spatial\u2013Temporal Event Prediction. Information Sciences 588 (2022) 315\u2013330.","DOI":"10.1016\/j.ins.2021.12.085"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"M.\u00a0E. Kahn. 2005. The Death Toll from Natural Disasters: The Role of Income Geography and Institutions. Review of Economics and Statistics 87 2 (2005) 271\u2013284.","DOI":"10.1162\/0034653053970339"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"D. Kandris C. Nakas D. Vomvas et\u00a0al. 2020. Applications of wireless sensor networks: an up-to-date survey. Applied System Innovation 3 1 (2020) 1\u201324.","DOI":"10.3390\/asi3010014"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"P. Kaur J.\u00a0C. Joshi and P. Aggarwal. 2022. A Multi-Model Decision Support System (MM-DSS) for Avalanche Hazard Prediction over North-West Himalaya. Natural Hazards 110 1 (2022) 563\u2013585.","DOI":"10.1007\/s11069-021-04958-5"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"S. Laxman and P.\u00a0S. Sastry. 2006. A Survey of Temporal Data Mining. Sadhana 31 (2006) 173\u2013198.","DOI":"10.1007\/BF02719780"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Mathieu Lepot Zoran Kapelan and Francois\u00a0HLR Clemens-Meyer. 2021. Design of a monitoring network: from macro to micro design. Metrology in Urban Drainage and Stormwater Management (2021) 155\u2013202.","DOI":"10.2166\/9781789060119_0155"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.5555\/2994430"},{"key":"e_1_3_3_1_19_2","first-page":"7","volume-title":"Proceedings","volume":"87","author":"Li Yao","year":"2023","unstructured":"Yao Li. 2023. Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling. In Proceedings , Vol.\u00a087. MDPI, 7\u201311."},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Y. Li T. Ge and C. Chen. 2020. Data Stream Event Prediction Based on Timing Knowledge and State Transitions. Proceedings of the VLDB Endowment 13 10 (2020) 1779\u20131792.","DOI":"10.14778\/3401960.3401973"},{"key":"e_1_3_3_1_21_2","unstructured":"J. Liu L. Min and X. Huang. 2021. An Overview of Event Extraction and Its Applications. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2111.03212 (2021)."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Q. Liu X. Wang X. Huang et\u00a0al. 2020. Prediction Model of Rock Mass Class Using Classification and Regression Tree Integrated AdaBoost Algorithm Based on TBM Driving Data. Tunnelling and Underground Space Technology 106 (2020) 1\u201313.","DOI":"10.1016\/j.tust.2020.103595"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"R.\u00a0A. Meyers. 2009. Encyclopedia of Complexity and Systems Science. Springer New York 1293\u20131295.","DOI":"10.1007\/978-0-387-30440-3"},{"key":"e_1_3_3_1_24_2","first-page":"10","volume-title":"International Conference on Machine Learning","author":"Muandet K.","year":"2013","unstructured":"K. Muandet, D. Balduzzi, and B. Sch\u00f6lkopf. 2013. Domain Generalization via Invariant Feature Representation. In International Conference on Machine Learning. PMLR, 10\u201318."},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"D.\u00a0H. Nguyen-Le Q.\u00a0B. Tao V.\u00a0H. Nguyen et\u00a0al. 2020. A Data-Driven Approach Based on Long Short-Term Memory and Hidden Markov Model for Crack Propagation Prediction. Engineering Fracture Mechanics 235 (2020) 1\u201322.","DOI":"10.1016\/j.engfracmech.2020.107085"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330937"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"crossref","unstructured":"M. Okawa T. Iwata Y. Tanaka et\u00a0al. 2022. Context-Aware Spatio-Temporal Event Prediction via Convolutional Hawkes Processes. Machine Learning 111 8 (2022) 2929\u20132950.","DOI":"10.1007\/s10994-022-06136-5"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01931"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Bikash Sadhukhan Shayak Chakraborty and Somenath Mukherjee. 2023. Predicting the magnitude of an impending earthquake using deep learning techniques. Earth Science Informatics 16 1 (2023) 803\u2013823.","DOI":"10.1007\/s12145-022-00916-2"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"crossref","unstructured":"G. Savcisens T. Eliassi-Rad L.\u00a0K. Hansen et\u00a0al. 2024. Using Sequences of Life-Events to Predict Human Lives. Nature Computational Science 4 1 (2024) 43\u201356.","DOI":"10.1038\/s43588-023-00573-5"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","unstructured":"Germans Savcisens Tina Eliassi-Rad Lars\u00a0Kai Hansen Laust\u00a0Hvas Mortensen Lau Lilleholt Anna Rogers Ingo Zettler and Sune Lehmann. 2024. Using sequences of life-events to predict human lives. Nature Computational Science 4 1 (2024) 43\u201356.","DOI":"10.1038\/s43588-023-00573-5"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01205"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"crossref","unstructured":"R. Sun G. Gao Z. Gong et\u00a0al. 2020. A Review of Risk Analysis Methods for Natural Disasters. Natural Hazards 100 2 (2020) 571\u2013593.","DOI":"10.1007\/s11069-019-03826-7"},{"key":"e_1_3_3_1_34_2","unstructured":"I. Sutskever. 2014. Sequence to Sequence Learning with Neural Networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1409.3215 (2014) 1\u20139."},{"key":"e_1_3_3_1_35_2","unstructured":"Mingxing Tan and Quoc\u00a0V Le. 2019. MixConv: Mixed Depthwise Convolutional Kernels. arXiv e-prints (2019) arXiv\u20131907."},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"crossref","unstructured":"S. Wang J. Cao and S.\u00a0Yu Philip. 2020. Deep Learning for Spatio-Temporal Data Mining: A Survey. IEEE Transactions on Knowledge and Data Engineering 34 8 (2020) 3681\u20133700.","DOI":"10.1109\/TKDE.2020.3025580"},{"key":"e_1_3_3_1_37_2","unstructured":"Yanxiang Wang Xian Zhang Yiran Shen Bowen Du Guangrong Zhao Lizhen Cui and Hongkai Wen. 2021. Event-stream representation for human gaits identification using deep neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 7 (2021) 3436\u20133449."},{"key":"e_1_3_3_1_38_2","first-page":"54","volume-title":"Machine Learning for Healthcare Conference","author":"Wu Zhiliang","year":"2021","unstructured":"Zhiliang Wu, Yinchong Yang, Peter\u00a0A Fashing, and Volker Tresp. 2021. Uncertainty-aware time-to-event prediction using deep kernel accelerated failure time models. In Machine Learning for Healthcare Conference. PMLR, 54\u201379."},{"key":"e_1_3_3_1_39_2","unstructured":"H. Xu H. Ren and Y. Song. 2022. Deformation Evolution and Failure Mechanism of Three-Point Bending Granite Specimen with Cracks. Chinese Journal of Underground Space and Engineering 18 4 (2022) 1199\u20131207+1218."},{"key":"e_1_3_3_1_40_2","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1810.00826 (2018) 1\u201317."},{"key":"e_1_3_3_1_41_2","doi-asserted-by":"crossref","unstructured":"L. Zhao. 2021. Event Prediction in the Big Data Era: A Systematic Survey. ACM Computing Surveys (CSUR) 54 5 (2021) 1\u201337.","DOI":"10.1145\/3450287"},{"key":"e_1_3_3_1_42_2","first-page":"1875","volume-title":"Proceedings of the 29th international conference on computational linguistics","author":"Zhou Bo","year":"2022","unstructured":"Bo Zhou, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, and Qiuxia Li. 2022. Generating temporally-ordered event sequences via event optimal transport. In Proceedings of the 29th international conference on computational linguistics. 1875\u20131884."},{"key":"e_1_3_3_1_43_2","doi-asserted-by":"crossref","unstructured":"Wen Zhou Yiwen Liang Xinan Wang Zhe Ming Zhenhua Xiao and Xiying Fan. 2022. Introducing macrophages to artificial immune systems for earthquake prediction. Applied Soft Computing 122 (2022) 108822.","DOI":"10.1016\/j.asoc.2022.108822"},{"key":"e_1_3_3_1_44_2","doi-asserted-by":"crossref","unstructured":"X. Zhou P. Lu Z. Zheng et\u00a0al. 2020. Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree. Reliability Engineering & System Safety 200 (2020) 1\u201320.","DOI":"10.1016\/j.ress.2020.106931"},{"key":"e_1_3_3_1_45_2","doi-asserted-by":"crossref","unstructured":"Zhou Zhou Gang Chen Ping Zhou Weibo Rao and Jifa Chen. 2025. Dynamically Tuned Variational Mode Decomposition and Convolutional Bidirectional Gated Recurrent Unit Algorithm for Coastal Sea Level Prediction. Journal of Marine Science and Engineering 13 11 (2025) 1\u201323.","DOI":"10.3390\/jmse13112055"}],"event":{"name":"EILM 2025: 2025 International Conference on Embodied Intelligence and Large Models","location":"Chengdu China","acronym":"EILM 2025"},"container-title":["Proceedings of the 2025 International Conference on Embodied Intelligence and Large Models"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3788731.3788752","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T12:09:31Z","timestamp":1771070971000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3788731.3788752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,19]]},"references-count":44,"alternative-id":["10.1145\/3788731.3788752","10.1145\/3788731"],"URL":"https:\/\/doi.org\/10.1145\/3788731.3788752","relation":{},"subject":[],"published":{"date-parts":[[2025,12,19]]},"assertion":[{"value":"2026-02-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}