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By forecasting individual elements of a directly-follows graph, the future state of the system can be predicted. However, the current state-of-the-art principally employs univariate forecasting of direct-follows relationships (DFs). This univariate approach overlooks the process structure and possible relations between different elements within the process. This paper introduces a comprehensive deployment of multivariate time series models, more specifically a range of different machine- and deep learning approaches, to forecast DFs. These are benchmarked on different event logs collected from real-life event processes. Our extensive experiments reveal that the performance of these forecasting models varies significantly across different processes, highlighting the importance of model selection.<\/jats:p>","DOI":"10.1007\/978-3-031-82225-4_21","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T03:06:20Z","timestamp":1743303980000},"page":"279-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multivariate Approaches for\u00a0Process Model Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2964-6611","authenticated-orcid":false,"given":"Yongbo","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4644-4881","authenticated-orcid":false,"given":"Jari","family":"Peeperkorn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0389-0275","authenticated-orcid":false,"given":"Johannes","family":"De Smedt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6151-0504","authenticated-orcid":false,"given":"Jochen","family":"De Weerdt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"21_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"21_CR2","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Challu, C., Olivares, K.G., Oreshkin, B.N., Ramirez, F.G., Canseco, M.M., Dubrawski, A.: NHITS: neural hierarchical interpolation for time series forecasting. 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