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Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115399_bib67","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"33","author":"Grill","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115399_bib68","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115399_bib69","unstructured":"Standard score. \u3008https:\/\/en.wikipedia.org\/wiki\/Standard_score\u3009 (Accessed 25 October 2025)."},{"issue":"3","key":"10.1016\/j.asoc.2026.115399_bib70","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X","article-title":"ARMA models and the Box\u2013Jenkins methodology","volume":"16","author":"Makridakis","year":"1997","journal-title":"J. Forecast."},{"issue":"1","key":"10.1016\/j.asoc.2026.115399_bib71","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1111\/j.1467-6419.2010.00637.x","article-title":"Rise of VAR modelling approach","volume":"25","author":"Qin","year":"2011","journal-title":"J. Econ. Surv."},{"key":"10.1016\/j.asoc.2026.115399_bib72","unstructured":"I. Sutskever, Sequence to sequence learning with neural networks, arXiv preprint arXiv:1409.3215, 2014."},{"key":"10.1016\/j.asoc.2026.115399_bib73","series-title":"Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13\u201316, 2018, Proceedings, Part I 25","first-page":"362","article-title":"Structured sequence modeling with graph convolutional recurrent networks","author":"Seo","year":"2018"},{"key":"10.1016\/j.asoc.2026.115399_bib74","unstructured":"Y. Li, R. Yu, C. Shahabi, Y. 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