{"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/set-functions-for-time-series-1","title":"Set Functions for Time Series","arxiv_id":"1909.12064","date":"2019-09-26","proceeding":"ICML 2020 1","authors":["Max Horn","Michael Moor","Christian Bock","Bastian Rieck","Karsten Borgwardt"],"abstract":"Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series with unaligned measurements, focusing on high scalability and data efficiency. Our method SeFT (Set Functions for Time Series) is based on recent advances in differentiable set function learning, extremely parallelizable with a beneficial memory footprint, thus scaling well to large datasets of long time series and online monitoring scenarios. Furthermore, our approach permits quantifying per-observation contributions to the classification outcome. We extensively compare our method with existing algorithms on multiple healthcare time series datasets and demonstrate that it performs competitively whilst significantly reducing runtime.","url_abs":"https://arxiv.org/abs/1909.12064v3","url_pdf":"https://arxiv.org/pdf/1909.12064v3.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":"set-functions-for-time-series-1","repo_url":"https://github.com/BorgwardtLab/Set_Functions_for_Time_Series","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"set-functions-for-time-series-1","repo_url":"https://github.com/WenjieDu/PyPOTS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"GRU-D","rank_in_archive_order":1,"of":28,"metrics":{"AUC":"86.99%","AUC Stdev":"0.22%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"Transformer","rank_in_archive_order":3,"of":28,"metrics":{"AUC":"86.28%","AUC Stdev":"0.35%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"IP-Nets","rank_in_archive_order":4,"of":28,"metrics":{"AUC":"86.24%","AUC Stdev":"0.38%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"SeFT-Attn","rank_in_archive_order":7,"of":28,"metrics":{"AUC":"85.14%","AUC Stdev":"0.13%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"GRU-Simple","rank_in_archive_order":13,"of":28,"metrics":{"AUC":"81.69%","AUC Stdev":"0.43%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"Phased-LSTM","rank_in_archive_order":14,"of":28,"metrics":{"AUC":"79.94%","AUC Stdev":"1.17%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1909.12064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.12064"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/BorgwardtLab/Set_Functions_for_Time_Series","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/WenjieDu/PyPOTS","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"cbd12e186fcc85ee","entry":"create_subfolder","repo":"BorgwardtLab/Set_Functions_for_Time_Series","repo_kind":"official","path":"seft/cli/fit_model.py","file_url":"https://github.com/BorgwardtLab/Set_Functions_for_Time_Series/blob/HEAD/seft/cli/fit_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"none","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"cbd12e186fcc85ee"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}