{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:03:25Z","timestamp":1752192205761,"version":"3.41.2"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42371476"],"award-info":[{"award-number":["42371476"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["buctrc202132"],"award-info":[{"award-number":["buctrc202132"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01232-4","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T08:46:04Z","timestamp":1752137164000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EventFormer: a hierarchical neural point process framework for spatio-temporal clustering events prediction"],"prefix":"10.1186","volume":"12","author":[{"given":"Shanshan","family":"Yu","sequence":"first","affiliation":[]},{"given":"Danhuai","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"1232_CR1","doi-asserted-by":"crossref","unstructured":"Yang SH, Long B, Smola A, Sadagopan N, Zheng Z, Zha H. Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the 20th international conference on World wide web; 2011. p. 537\u201346.","DOI":"10.1145\/1963405.1963481"},{"issue":"01","key":"1232_CR2","doi-asserted-by":"publisher","first-page":"1550005","DOI":"10.1142\/S2382626615500057","volume":"1","author":"E Bacry","year":"2015","unstructured":"Bacry E, Mastromatteo I, Muzy JF. Hawkes processes in finance. Market Microstruct Liq. 2015;1(01):1550005.","journal-title":"Market Microstruct Liq"},{"key":"1232_CR3","doi-asserted-by":"crossref","unstructured":"Wang L, Zhang W, He X, Zha H. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining; 2018. p. 2447\u201356.","DOI":"10.1145\/3219819.3219961"},{"issue":"1","key":"1232_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s40537-023-00707-6","volume":"10","author":"X Shi","year":"2023","unstructured":"Shi X, Liu Q, Bai Y, Shang M. RTiSR: a review-driven time interval-aware sequential recommendation method. J Big Data. 2023;10(1):32.","journal-title":"J Big Data"},{"key":"1232_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126249","volume":"542","author":"Z Wang","year":"2023","unstructured":"Wang Z, Ding D, Ren M, Conti M. TANGO: a temporal spatial dynamic graph model for event prediction. Neurocomputing. 2023;542: 126249.","journal-title":"Neurocomputing."},{"key":"1232_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119779","volume":"222","author":"X Huang","year":"2023","unstructured":"Huang X, Ye Y, Yang X, Xiong L. Multi-view dynamic graph convolution neural network for traffic flow prediction. Expert Syst Appl. 2023;222: 119779.","journal-title":"Expert Syst Appl"},{"issue":"9","key":"1232_CR7","doi-asserted-by":"publisher","first-page":"e2023EF003777","DOI":"10.1029\/2023EF003777","volume":"11","author":"S Stockman","year":"2023","unstructured":"Stockman S, Lawson DJ, Werner MJ. Forecasting the 2016\u20132017 Central Apennines earthquake sequence with a neural point process. Earth\u2019s Future. 2023;11(9):e2023EF003777.","journal-title":"Earth\u2019s Future."},{"key":"1232_CR8","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et\u00a0al. Attention is all you need. Advances in neural information processing systems; 2017. p. 30."},{"key":"1232_CR9","first-page":"1","volume":"44","author":"C Palm","year":"1943","unstructured":"Palm C. Intensitatsschwankungen im fernsprechverker. Ericsson Technics. 1943;44:1\u2013189.","journal-title":"Ericsson Technics."},{"issue":"1","key":"1232_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/biomet\/58.1.83","volume":"58","author":"AG Hawkes","year":"1971","unstructured":"Hawkes AG. Spectra of some self-exciting and mutually exciting point processes. Biometrika. 1971;58(1):83\u201390.","journal-title":"Biometrika."},{"issue":"2","key":"1232_CR11","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.ijforecast.2021.07.001","volume":"38","author":"WH Chiang","year":"2022","unstructured":"Chiang WH, Liu X, Mohler G. Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates. Int J Forecast. 2022;38(2):505\u201320.","journal-title":"Int J Forecast"},{"key":"1232_CR12","doi-asserted-by":"publisher","first-page":"2667","DOI":"10.1109\/TIFS.2020.2970601","volume":"15","author":"HS Dutta","year":"2020","unstructured":"Dutta HS, Dutta VR, Adhikary A, Chakraborty T. HawkesEye: detecting fake retweeters using Hawkes process and topic modeling. IEEE Trans Inf Forens Secur. 2020;15:2667\u201378.","journal-title":"IEEE Trans Inf Forens Secur"},{"issue":"2","key":"1232_CR13","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1080\/14697688.2017.1403131","volume":"18","author":"AG Hawkes","year":"2018","unstructured":"Hawkes AG. Hawkes processes and their applications to finance: a review. Quant Financ. 2018;18(2):193\u20138.","journal-title":"Quant Financ"},{"key":"1232_CR14","doi-asserted-by":"crossref","unstructured":"Rizoiu MA, Lee Y, Mishra S, Xie L. Hawkes processes for events in social media. In: Frontiers of multimedia research; 2017. p. 191\u2013218.","DOI":"10.1145\/3122865.3122874"},{"key":"1232_CR15","doi-asserted-by":"crossref","unstructured":"Gu Y. Attentive neural point processes for event forecasting. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a035; 2021. p. 7592\u2013600.","DOI":"10.1609\/aaai.v35i9.16929"},{"key":"1232_CR16","doi-asserted-by":"crossref","unstructured":"Jiang L, Wang P, Cheng K, Liu K, Yin M, Jin B, et\u00a0al. Eduhawkes: a neural hawkes process approach for online study behavior modeling. In: Proceedings of the 2021 SIAM international conference on data mining (SDM). SIAM; 2021. p. 567\u201375.","DOI":"10.1137\/1.9781611976700.64"},{"key":"1232_CR17","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.neucom.2022.02.028","volume":"485","author":"Y Ru","year":"2022","unstructured":"Ru Y, Qiu X, Tan X, Chen B, Gao Y, Jin Y. Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing. 2022;485:114\u201323.","journal-title":"Neurocomputing."},{"key":"1232_CR18","unstructured":"Zhang W, Panum T, Jha S, Chalasani P, Page D. Cause: learning granger causality from event sequences using attribution methods. In: International conference on machine learning. PMLR; 2020. p. 11235\u201345."},{"key":"1232_CR19","first-page":"17311","volume":"34","author":"T Gao","year":"2021","unstructured":"Gao T, Subramanian D, Bhattacharjya D, Shou X, Mattei N, Bennett KP. Causal inference for event pairs in multivariate point processes. Adv Neural Inf Process Syst. 2021;34:17311\u201324.","journal-title":"Adv Neural Inf Process Syst."},{"key":"1232_CR20","unstructured":"Zhang DC, Lauw H. Dynamic topic models for temporal document networks. In: International conference on machine learning. PMLR; 2022. p. 26281\u201392."},{"key":"1232_CR21","unstructured":"He X, Rekatsinas T, Foulds J, Getoor L, Liu Y. Hawkestopic: A joint model for network inference and topic modeling from text-based cascades. In: International conference on machine learning. PMLR; 2015. p. 871\u201380."},{"key":"1232_CR22","doi-asserted-by":"crossref","unstructured":"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 the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016. p. 1555\u201364.","DOI":"10.1145\/2939672.2939875"},{"key":"1232_CR23","doi-asserted-by":"crossref","unstructured":"Xiao S, Yan J, Yang X, Zha H, Chu S. Modeling the intensity function of point process via recurrent neural networks. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a031; 2017. https:\/\/api.semanticscholar.org\/CorpusID:7487003.","DOI":"10.1609\/aaai.v31i1.10724"},{"key":"1232_CR24","unstructured":"Mei H, Eisner JM. The neural hawkes process: A neurally self-modulating multivariate point process. Advances in neural information processing systems; 2017. p. 30."},{"key":"1232_CR25","unstructured":"Zhang Q, Lipani A, Kirnap O, Yilmaz E. Self-attentive Hawkes process. In: International conference on machine learning. PMLR; 2020. p. 11183\u201393."},{"key":"1232_CR26","unstructured":"Zuo S, Jiang H, Li Z, Zhao T, Zha H. Transformer hawkes process. In: International conference on machine learning. PMLR; 2020. p. 11692\u2013702."},{"key":"1232_CR27","unstructured":"Omi T, Aihara K, et\u00a0al. Fully neural network based model for general temporal point processes. Advances in neural information processing systems; 2019. p. 32."},{"key":"1232_CR28","doi-asserted-by":"publisher","unstructured":"Yang C, Mei H, Eisner J. Transformer embeddings of irregularly spaced events and their participants; 2021. arXiv preprint arXiv:2201.00044. https:\/\/doi.org\/10.48550\/arXiv.2201.00044.","DOI":"10.48550\/arXiv.2201.00044"},{"key":"1232_CR29","doi-asserted-by":"crossref","unstructured":"Ding F, Yan J, Wang H. c-NTPP: learning cluster-aware neural temporal point process. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a037; 2023. p. 7369\u201377.","DOI":"10.1609\/aaai.v37i6.25897"},{"key":"1232_CR30","doi-asserted-by":"crossref","unstructured":"Shang J, Sun M. Geometric hawkes processes with graph convolutional recurrent neural networks. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a033; 2019. p. 4878\u201385.","DOI":"10.1609\/aaai.v33i01.33014878"},{"key":"1232_CR31","doi-asserted-by":"publisher","unstructured":"Shchur O, Bilo\u0161 M, G\u00fcnnemann S. Intensity-free learning of temporal point processes; 2019. arXiv preprint arXiv:1909.12127. https:\/\/doi.org\/10.48550\/arXiv.1909.12127.","DOI":"10.48550\/arXiv.1909.12127"},{"key":"1232_CR32","doi-asserted-by":"publisher","unstructured":"Chen RT, Amos B, Nickel M. Neural spatio-temporal point processes; 2020. arXiv preprint arXiv:2011.04583. https:\/\/doi.org\/10.48550\/arXiv.2011.04583.","DOI":"10.48550\/arXiv.2011.04583"},{"key":"1232_CR33","unstructured":"Zhou Z, Yang X, Rossi R, Zhao H, Yu R. Neural point process for learning spatiotemporal event dynamics. In: Learning for dynamics and control conference. PMLR; 2022. p. 777\u201389."},{"key":"1232_CR34","doi-asserted-by":"crossref","unstructured":"Yuan Y, Ding J, Shao C, Jin D, Li Y. Spatio-temporal diffusion point processes. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining; 2023. p. 3173\u201384.","DOI":"10.1145\/3580305.3599511"},{"issue":"10","key":"1232_CR35","doi-asserted-by":"publisher","first-page":"5223","DOI":"10.1109\/JBHI.2022.3193148","volume":"26","author":"Y Tang","year":"2022","unstructured":"Tang Y, Zhang L, Wu H, He J, Song A. Dual-branch interactive networks on multichannel time series for human activity recognition. IEEE J Biomed Health Inform. 2022;26(10):5223\u201334.","journal-title":"IEEE J Biomed Health Inform."},{"key":"1232_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2022.108642","volume":"145","author":"J Shi","year":"2023","unstructured":"Shi J, Gao Y, Gu D, Li Y, Chen K. A novel approach to detect electricity theft based on conv-attentional transformer neural network. Int J Electr Power Energy Syst. 2023;145: 108642.","journal-title":"Int J Electr Power Energy Syst."},{"key":"1232_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107420","volume":"165","author":"Y Mourchid","year":"2023","unstructured":"Mourchid Y, Slama R. D-stgcnt: a dense spatio-temporal graph conv-gru network based on transformer for assessment of patient physical rehabilitation. Comput Biol Med. 2023;165: 107420.","journal-title":"Comput Biol Med."},{"key":"1232_CR38","doi-asserted-by":"crossref","unstructured":"Yu G, Li A, Zheng C, Guo Y, Wang Y, Wang H. Dual-branch attention-in-attention transformer for single-channel speech enhancement. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE; 2022. p. 7847\u201351.","DOI":"10.1109\/ICASSP43922.2022.9746273"},{"key":"1232_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2022.116138","volume":"269","author":"Z Shao","year":"2022","unstructured":"Shao Z, Han J, Zhao W, Zhou K, Yang S. Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field. Energy Convers Manag. 2022;269: 116138.","journal-title":"Energy Convers Manag."},{"key":"1232_CR40","doi-asserted-by":"crossref","unstructured":"Fang Y, Liu R, Huang H, Zhao P, Wu Q. A Spatio-Temporal Diffusion Model for Missing and Real-Time Financial Data Inference. In: Proceedings of the 33rd ACM international conference on information and knowledge management; 2024. p. 602\u201311.","DOI":"10.1145\/3627673.3679806"},{"issue":"5","key":"1232_CR41","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1080\/00207179.2020.1849807","volume":"95","author":"A Chunxiang","year":"2022","unstructured":"Chunxiang A, Shen Y, Zeng Y. Dynamic asset-liability management problem in a continuous-time model with delay. Int J Control. 2022;95(5):1315\u201336.","journal-title":"Int J Control."},{"issue":"1","key":"1232_CR42","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/s12874-022-01679-6","volume":"22","author":"K Suresh","year":"2022","unstructured":"Suresh K, Severn C, Ghosh D. Survival prediction models: an introduction to discrete-time modeling. BMC Med Res Methodol. 2022;22(1):207.","journal-title":"BMC Med Res Methodol."},{"issue":"6","key":"1232_CR43","doi-asserted-by":"publisher","first-page":"5626","DOI":"10.1109\/TIE.2022.3199933","volume":"70","author":"T Ma","year":"2022","unstructured":"Ma T, Jiang C, Xiang J, Wang X, Chau K, Long T. Modeling and analysis of wireless power transfer system via unified full-load discrete-time model. IEEE Trans Ind Electr. 2022;70(6):5626\u201336.","journal-title":"IEEE Trans Ind Electr."},{"key":"1232_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3071-5","volume-title":"Monte Carlo statistical methods","author":"CP Robert","year":"1999","unstructured":"Robert CP, Casella G, Casella G. Monte Carlo statistical methods, vol. 2. Springer; 1999."},{"issue":"5","key":"1232_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3465055","volume":"12","author":"S Chaudhari","year":"2021","unstructured":"Chaudhari S, Mithal V, Polatkan G, Ramanath R. An attentive survey of attention models. ACM Trans Intell Syst Technol (TIST). 2021;12(5):1\u201332.","journal-title":"ACM Trans Intell Syst Technol (TIST)."},{"issue":"1","key":"1232_CR46","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1186\/s40537-021-00544-5","volume":"8","author":"F Laghrissi","year":"2021","unstructured":"Laghrissi F, Douzi S, Douzi K, Hssina B. IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism. J Big Data. 2021;8(1):149.","journal-title":"J Big Data."},{"key":"1232_CR47","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2021. p. 13713\u201322.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"1232_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120210","volume":"225","author":"Z Kaddari","year":"2023","unstructured":"Kaddari Z, Bouchentouf T. A novel self-attention enriching mechanism for biomedical question answering. Expert Syst Appl. 2023;225: 120210.","journal-title":"Expert Syst Appl."},{"key":"1232_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122365","volume":"239","author":"J Ma","year":"2024","unstructured":"Ma J, Hu S, Fu J, Chen G. A hierarchical attention detector for bearing surface defect detection. Expert Syst Appl. 2024;239: 122365.","journal-title":"Expert Syst Appl."},{"key":"1232_CR50","unstructured":"Wang C, Li M, Smola AJ. Language models with transformers; 2019. arXiv preprint arXiv:1904.09408."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01232-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01232-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01232-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T08:46:16Z","timestamp":1752137176000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01232-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":50,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1232"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01232-4","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"27 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"162"}}