{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/warpformer-a-multi-scale-modeling-approach","title":"Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series","arxiv_id":"2306.09368","date":"2023-06-14","proceeding":null,"authors":["Jiawen Zhang","Shun Zheng","Wei Cao","Jiang Bian","Jia Li"],"abstract":"Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy. Intra-series irregularity refers to the fact that time-series signals are often recorded at irregular intervals, while inter-series discrepancy refers to the significant variability in sampling rates among diverse series. However, recent advances in irregular time series have primarily focused on addressing intra-series irregularity, overlooking the issue of inter-series discrepancy. To bridge this gap, we present Warpformer, a novel approach that fully considers these two characteristics. In a nutshell, Warpformer has several crucial designs, including a specific input representation that explicitly characterizes both intra-series irregularity and inter-series discrepancy, a warping module that adaptively unifies irregular time series in a given scale, and a customized attention module for representation learning. Additionally, we stack multiple warping and attention modules to learn at different scales, producing multi-scale representations that balance coarse-grained and fine-grained signals for downstream tasks. We conduct extensive experiments on widely used datasets and a new large-scale benchmark built from clinical databases. The results demonstrate the superiority of Warpformer over existing state-of-the-art approaches.","url_abs":"https://arxiv.org/abs/2306.09368v1","url_pdf":"https://arxiv.org/pdf/2306.09368v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"warpformer-a-multi-scale-modeling-approach","repo_url":"https://github.com/imjiawen/warpformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"irregular-time-series","task_name":"Irregular Time Series"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"time-series-1","task_name":"Time Series"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2306.09368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09368"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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