{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:49:20Z","timestamp":1777873760540,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","funder":[{"name":"Ministry of Economic Development of the Russian Federation","award":["25-139-66879-1-0003"],"award-info":[{"award-number":["25-139-66879-1-0003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737428","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T21:04:26Z","timestamp":1754255066000},"page":"5730-5741","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["EBES: Easy Benchmarking for Event Sequences"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2504-2479","authenticated-orcid":false,"given":"Dmitry","family":"Osin","sequence":"first","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7909-9668","authenticated-orcid":false,"given":"Igor","family":"Udovichenko","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Moscow, Russian Federation and Vega Institute Foundation, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1782-6290","authenticated-orcid":false,"given":"Egor","family":"Shvetsov","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5119-8980","authenticated-orcid":false,"given":"Viktor","family":"Moskvoretskii","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Moscow, Russian Federation and Higher School of Economics, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0690","authenticated-orcid":false,"given":"Evgeny","family":"Burnaev","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Moscow, Russian Federation and Artificial Intelligence Research Institute, Moscow, Russian Federation"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","article-title":"Predicting Customer Segment Changes to Enhance Customer Retention: A Case Study for Online Retail using Machine Learning","volume":"14","author":"Lahcen ABIDAR","year":"2023","unstructured":"Lahcen ABIDAR, Dounia ZAIDOUNI, EL Ikram, and Abdeslam ENNOUAARY. 2023. Predicting Customer Segment Changes to Enhance Customer Retention: A Case Study for Online Retail using Machine Learning. International Journal of Advanced Computer Science and Applications, Vol. 14, 7 (2023).","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_2_3_1","unstructured":"Christian Arnold Luka Biedebach Andreas K\u00fcpfer and Marcel Neunhoeffer. 2023. The role of hyperparameters in machine learning models and how to tune them. Political Science Research and Methods(2023) 1-8."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526129"},{"key":"e_1_3_2_2_5_1","volume-title":"Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh.","author":"Bagnall Anthony","year":"2018","unstructured":"Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018. The UEA multivariate time series classification archive. arXiv preprint arXiv:1811.00075(2018)."},{"key":"e_1_3_2_2_6_1","unstructured":"Alexandra Bazarova Maria Kovaleva Ilya Kuleshov Evgenia Romanenkova Alexander Stepikin Alexandr Yugay Dzhambulat Mollaev Ivan Kireev Andrey Savchenko and Alexey Zaytsev. 2024. Universal representations for financial transactional data: embracing local global and external contexts. arXiv e-prints(2024) arXiv-2404."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"e_1_3_2_2_8_1","volume-title":"Recurrent neural networks for multivariate time series with missing values. Scientific reports","author":"Che Zhengping","year":"2018","unstructured":"Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports, Vol. 8, 1 (2018), 6085."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25876"},{"key":"e_1_3_2_2_10_1","unstructured":"Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1890\/03-0520"},{"key":"e_1_3_2_2_12_1","unstructured":"Philip Darke Paolo Missier and Jaume Bacardit. 2022. Benchmark time series data sets for PyTorch-the torchtime package. arXiv preprint arXiv:2207.12503(2022)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911747"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Mollaev Dzhambulat Alexander Kostin Postnova Maria Ivan Karpukhin Ivan A Kireev Gleb Gusev and Andrey Savchenko. 2024. Multimodal Banking Dataset: Understanding Client Needs through Event Sequences. arXiv:2409.17587 [cs.LG] https:\/\/arxiv.org\/abs\/2409.17587","DOI":"10.1145\/3746252.3761635"},{"key":"e_1_3_2_2_15_1","volume-title":"Geoffrey I Webb, and Mahsa Salehi.","author":"Foumani Navid Mohammadi","year":"2024","unstructured":"Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I Webb, and Mahsa Salehi. 2024. Improving position encoding of transformers for multivariate time series classification. Data mining and knowledge discovery, Vol. 38, 1 (2024), 22-48."},{"key":"e_1_3_2_2_16_1","unstructured":"Fabrizio Garuti Enver Sangineto Simone Luetto Lorenzo Forni and Rita Cucchiara. 2025. Diffusion Transformers for Tabular Data Time Series Generation. arXiv preprint arXiv:2504.07566(2025)."},{"key":"e_1_3_2_2_17_1","volume-title":"Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley.","author":"Goldberger Ary L","year":"2000","unstructured":"Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, Vol. 101, 23 (2000), e215-e220."},{"key":"e_1_3_2_2_18_1","volume-title":"Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752(2023).","author":"Gu Albert","year":"2023","unstructured":"Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752(2023)."},{"key":"e_1_3_2_2_19_1","unstructured":"Odd Erik Gundersen Kevin Coakley Christine Kirkpatrick and Yolanda Gil. 2022. Sources of irreproducibility in machine learning: A review. arXiv preprint arXiv:2204.07610(2022)."},{"key":"e_1_3_2_2_20_1","volume-title":"International Conference on Machine Learning. PMLR, 4353-4363","author":"Horn Max","year":"2020","unstructured":"Max Horn, Michael Moor, Christian Bock, Bastian Rieck, and Karsten Borgwardt. 2020. Set functions for time series. In International Conference on Machine Learning. PMLR, 4353-4363."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-020-00727-0"},{"key":"e_1_3_2_2_22_1","volume-title":"Leo Anthony Celi, and Roger G Mark","author":"Johnson Alistair EW","year":"2016","unstructured":"Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data, Vol. 3, 1 (2016), 1-9."},{"key":"e_1_3_2_2_23_1","unstructured":"Ivan Karpukhin Foma Shipilov and Andrey Savchenko. 2024. HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting? arXiv preprint arXiv:2406.14341(2024)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3169897"},{"key":"e_1_3_2_2_25_1","first-page":"408","article-title":"Lives of eminent philosophers, translated by RD Hicks","volume":"2","author":"Laertius Diogenes","year":"1925","unstructured":"Diogenes Laertius. 1925. Lives of eminent philosophers, translated by RD Hicks. Loeb Classical Library, Vol. 2 (1925), 408-423.","journal-title":"Loeb Classical Library"},{"key":"e_1_3_2_2_26_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Li Zekun","year":"2024","unstructured":"Zekun Li, Shiyang Li, and Xifeng Yan. 2024. Time Series as Images: Vision Transformer for Irregularly Sampled Time Series. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_27_1","unstructured":"Michael A Lones. 2021. How to avoid machine learning pitfalls: a guide for academic researchers. arXiv preprint arXiv:2108.02497(2021)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Henry B Mann and Donald R Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics(1947) 50-60.","DOI":"10.1214\/aoms\/1177730491"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2017.2772256"},{"key":"e_1_3_2_2_30_1","volume-title":"MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences. arXiv preprint arXiv:2401.15935(2024).","author":"Moskvoretskii Viktor","year":"2024","unstructured":"Viktor Moskvoretskii, Dmitry Osin, Egor Shvetsov, Igor Udovichenko, Maxim Zhelnin, Andrey Dukhovny, Anna Zhimerikina, and Evgeny Burnaev. 2024a. MLEM: Generative and Contrastive Learning as Distinct Modalities for Event Sequences. arXiv preprint arXiv:2401.15935(2024)."},{"key":"e_1_3_2_2_31_1","unstructured":"Viktor Moskvoretskii Dmitry Osin Egor Shvetsov Igor Udovichenko Maxim Zhelnin Andrey Dukhovny Anna Zhimerikina Albert Efimov and Evgeny Burnaev. 2024b. Self-Supervised Learning in Event Sequences: A Comparative Study and Hybrid Approach of Generative Modeling and Contrastive Learning. arXiv:2401.15935 [cs.LG]"},{"key":"e_1_3_2_2_32_1","volume-title":"Early prediction of sepsis from clinical data: the PhysioNet\/Computing in Cardiology Challenge","author":"Reyna Matthew A","year":"2019","unstructured":"Matthew A Reyna, Christopher S Josef, Russell Jeter, Supreeth P Shashikumar, M Brandon Westover, Shamim Nemati, Gari D Clifford, and Ashish Sharma. 2020. Early prediction of sepsis from clinical data: the PhysioNet\/Computing in Cardiology Challenge 2019. Critical care medicine, Vol. 48, 2 (2020), 210-217."},{"key":"e_1_3_2_2_33_1","volume-title":"Interpolation-Prediction Networks for Irregularly Sampled Time Series. In International Conference on Learning Representations.","author":"Shukla Satya Narayan","year":"2018","unstructured":"Satya Narayan Shukla and Benjamin Marlin. 2018. Interpolation-Prediction Networks for Irregularly Sampled Time Series. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_34_1","volume-title":"Multi-Time Attention Networks for Irregularly Sampled Time Series. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4c0J6lwQ4_","author":"Shukla Satya Narayan","year":"2021","unstructured":"Satya Narayan Shukla and Benjamin Marlin. 2021. Multi-Time Attention Networks for Irregularly Sampled Time Series. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4c0J6lwQ4_"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3349497"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","unstructured":"Boudewijn van Dongen. 2017. BPI Challenge 2017. doi:10.4121\/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b","DOI":"10.4121\/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b"},{"key":"e_1_3_2_2_37_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_38_1","volume-title":"Time-aware attention-based gated network for credit card fraud detection by extracting transactional behaviors","author":"Xie Yu","year":"2022","unstructured":"Yu Xie, Guanjun Liu, Chungang Yan, Changjun Jiang, and MengChu Zhou. 2022. Time-aware attention-based gated network for credit card fraud detection by extracting transactional behaviors. IEEE Transactions on Computational Social Systems(2022)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7439(00)00122-2"},{"key":"e_1_3_2_2_40_1","volume-title":"EasyTPP: Towards Open Benchmarking Temporal Point Processes. In International Conference on Learning Representations (ICLR). https:\/\/arxiv.org\/abs\/2307","author":"Xue Siqiao","year":"2024","unstructured":"Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, and Hongyuan Mei. 2024. EasyTPP: Towards Open Benchmarking Temporal Point Processes. In International Conference on Learning Representations (ICLR). https:\/\/arxiv.org\/abs\/2307.08097"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3593582"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737428","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:17:10Z","timestamp":1777573030000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":41,"alternative-id":["10.1145\/3711896.3737428","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737428","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}