{"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/latent-odes-for-irregularly-sampled-time","title":"Latent ODEs for Irregularly-Sampled Time Series","arxiv_id":"1907.03907","date":"2019-07-08","proceeding":null,"authors":["Yulia Rubanova","Ricky T. Q. Chen","David Duvenaud"],"abstract":"Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs), a model we call ODE-RNNs. Furthermore, we use ODE-RNNs to replace the recognition network of the recently-proposed Latent ODE model. Both ODE-RNNs and Latent ODEs can naturally handle arbitrary time gaps between observations, and can explicitly model the probability of observation times using Poisson processes. We show experimentally that these ODE-based models outperform their RNN-based counterparts on irregularly-sampled data.","url_abs":"https://arxiv.org/abs/1907.03907v1","url_pdf":"https://arxiv.org/pdf/1907.03907v1.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":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/YuliaRubanova/latent_ode","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/BorealisAI/continuous-time-flow-process","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/HerreraKrachTeichmann/ControlledODERNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/HerreraKrachTeichmann/NJODE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/Ldhlwh/Latent-ODE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/MeetGandhi/Reconstruction-of-Trajectory-recorded-with-Missing-Markers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/ashysheya/ODE-RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/gkrudah/ODEnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/jacobjinkelly/easy-neural-ode","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/patrick-kidger/torchcde","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"latent-odes-for-irregularly-sampled-time","repo_url":"https://github.com/westny/neural-stability","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"multivariate-time-series-imputation","task_name":"Multivariate Time Series Imputation"},{"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/multivariate-time-series-forecasting-on-1","task":"Multivariate Time Series Forecasting","dataset":"MuJoCo","model":"Latent ODE (ODE enc)","rank_in_archive_order":1,"of":6,"metrics":{"MSE (10^-2, 50% missing)":"1.258"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-1","task":"Multivariate Time Series Forecasting","dataset":"MuJoCo","model":"ODE-RNN","rank_in_archive_order":5,"of":6,"metrics":{"MSE (10^-2, 50% missing)":"26.463"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-2","task":"Multivariate Time Series Forecasting","dataset":"PhysioNet Challenge 2012","model":"Latent ODE + Poisson","rank_in_archive_order":1,"of":4,"metrics":{"MSE stdev":"0.05","mse (10^-3)":"2.208"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-2","task":"Multivariate Time Series Forecasting","dataset":"PhysioNet Challenge 2012","model":"Latent ODE (ODE enc)","rank_in_archive_order":2,"of":4,"metrics":{"MSE stdev":"0.029","mse (10^-3)":"2.231"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-mujoco","task":"Multivariate Time Series Imputation","dataset":"MuJoCo","model":"Latent ODE (ODE enc)","rank_in_archive_order":1,"of":6,"metrics":{"MSE (10^2, 50% missing)":"0.285"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-mujoco","task":"Multivariate Time Series Imputation","dataset":"MuJoCo","model":"ODE-RNN","rank_in_archive_order":3,"of":6,"metrics":{"MSE (10^2, 50% missing)":"0.665"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-1","task":"Multivariate Time Series Imputation","dataset":"PhysioNet Challenge 2012","model":"Latent ODE (ODE enc)","rank_in_archive_order":6,"of":9,"metrics":{"mse (10^-3)":"2.118"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-imputation-on-1","task":"Multivariate Time Series Imputation","dataset":"PhysioNet Challenge 2012","model":"Latent ODE + Poisson","rank_in_archive_order":7,"of":9,"metrics":{"mse (10^-3)":"2.789"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"ODE-RNN","rank_in_archive_order":10,"of":28,"metrics":{"AUC":"83.3%","AUC Stdev":"0.9%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"Latent ODE (ODE enc","rank_in_archive_order":11,"of":28,"metrics":{"AUC":"82.9%","AUC Stdev":"0.4%"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-classification-on-physionet","task":"Time Series Classification","dataset":"PhysioNet Challenge 2012","model":"Latent ODE + Poisson","rank_in_archive_order":12,"of":28,"metrics":{"AUC":"82.6%","AUC Stdev":"0.7%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1907.03907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.03907"}},"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/patrick-kidger/torchcde","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ashysheya/ODE-RNN","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/westny/neural-stability","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jacobjinkelly/easy-neural-ode","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/HerreraKrachTeichmann/ControlledODERNN","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MeetGandhi/Reconstruction-of-Trajectory-recorded-with-Missing-Markers","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Ldhlwh/Latent-ODE","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/BorealisAI/continuous-time-flow-process","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/YuliaRubanova/latent_ode","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/gkrudah/ODEnet","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/HerreraKrachTeichmann/NJODE","reach":null}],"summary":{"ran_draft_wrong":3,"ran":1,"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":1,"repositories":1},"listed":{"samples":4,"ran":3,"repositories":2}},"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":3,"samples":[{"code_sha256_prefix":"7b0725913f7abbbb","entry":"sample_rademacher_like","repo":"BorealisAI/continuous-time-flow-process","repo_kind":"listed","path":"lib/layers/odefunc.py","file_url":"https://github.com/BorealisAI/continuous-time-flow-process/blob/HEAD/lib/layers/odefunc.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"7b0725913f7abbbb"}},{"code_sha256_prefix":"fc9a64735be0911c","entry":"create_classifier","repo":"gkrudah/ODEnet","repo_kind":"listed","path":"lib/base_models.py","file_url":"https://github.com/gkrudah/ODEnet/blob/HEAD/lib/base_models.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fc9a64735be0911c"}},{"code_sha256_prefix":"b03151f58b6fe757","entry":"divergence_bf","repo":"BorealisAI/continuous-time-flow-process","repo_kind":"listed","path":"lib/layers/odefunc.py","file_url":"https://github.com/BorealisAI/continuous-time-flow-process/blob/HEAD/lib/layers/odefunc.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"b03151f58b6fe757"}},{"code_sha256_prefix":"97cefd1ecd9fcbca","entry":"get_data_min_max","repo":"YuliaRubanova/latent_ode","repo_kind":"official","path":"physionet.py","file_url":"https://github.com/YuliaRubanova/latent_ode/blob/HEAD/physionet.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"97cefd1ecd9fcbca"}},{"code_sha256_prefix":"4128b3f4ab1a5a81","entry":"assign_value_or_sample","repo":"YuliaRubanova/latent_ode","repo_kind":"official","path":"generate_timeseries.py","file_url":"https://github.com/YuliaRubanova/latent_ode/blob/HEAD/generate_timeseries.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4128b3f4ab1a5a81"}},{"code_sha256_prefix":"f3c69a82cb618229","entry":"divergence_approx","repo":"BorealisAI/continuous-time-flow-process","repo_kind":"listed","path":"lib/layers/odefunc.py","file_url":"https://github.com/BorealisAI/continuous-time-flow-process/blob/HEAD/lib/layers/odefunc.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"f3c69a82cb618229"}},{"code_sha256_prefix":"dd0fe112d9664002","entry":"generate_periodic","repo":"YuliaRubanova/latent_ode","repo_kind":"official","path":"generate_timeseries.py","file_url":"https://github.com/YuliaRubanova/latent_ode/blob/HEAD/generate_timeseries.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dd0fe112d9664002"}},{"code_sha256_prefix":"14d893a51ceea2f1","entry":"get_next_val","repo":"YuliaRubanova/latent_ode","repo_kind":"official","path":"generate_timeseries.py","file_url":"https://github.com/YuliaRubanova/latent_ode/blob/HEAD/generate_timeseries.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"14d893a51ceea2f1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}