Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics

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dc.contributor.authorZheng, L.N.
dc.contributor.authorLi, Z.
dc.contributor.authorDong, C.G.
dc.contributor.authorZhang, W.E.
dc.contributor.authorYue, L.
dc.contributor.authorXu, M.
dc.contributor.authorMaennel, O.
dc.contributor.authorChen, W.
dc.contributor.conference33rd ACM International Conference on Information and Knowledge Management (CIKM) (21 Oct 2024 - 25 Oct 2024 : Boise, Idaho, USA)
dc.date.issued2024
dc.description.abstractIrregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4%, 94.9%, 60% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git.
dc.description.statementofresponsibilityLiangwei Nathan Zheng, Zhengyang Li, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
dc.identifier.citationProceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024), 2024, pp.3405-3414
dc.identifier.doi10.1145/3627673.3679716
dc.identifier.issn2155-0751
dc.identifier.orcidZheng, L.N. [0009-0007-2793-8110]
dc.identifier.orcidDong, C.G. [0009-0005-1495-6534]
dc.identifier.orcidZhang, W.E. [0000-0002-0406-5974]
dc.identifier.orcidYue, L. [0000-0001-9086-1805] [0000-0003-3007-1347]
dc.identifier.orcidMaennel, O. [0000-0002-9621-0787]
dc.identifier.orcidChen, W. [0000-0003-1001-7925]
dc.identifier.urihttps://hdl.handle.net/2440/148010
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.granthttp://purl.org/au-research/grants/arc/LP230200821
dc.relation.granthttp://purl.org/au-research/grants/arc/DP240103070
dc.relation.granthttp://purl.org/au-research/grants/arc/IE230100119
dc.relation.granthttp://purl.org/au-research/grants/arc/IE240100275
dc.rights© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.
dc.source.urihttps://dl.acm.org/doi/proceedings/10.1145/3627673
dc.subjectIrregular Time Series Data; Medical Machine Learning; Data Mining
dc.titleIrregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
dc.typeConference paper
pubs.publication-statusPublished

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