{"url":"/task/irregular-time-series","name":"Irregular Time Series","slug":"irregular-time-series","description_markdown":"Irregular Time Series","categories":[{"name":"Time Series","url":"/area/time-series"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":82,"papers_with_code":49,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[{"url":"/dataset/hurricane","name":"Extreme Events > Natural Disasters > Hurricane","full_name":"Tourism > Finance > Sales Revenue","num_papers_in_archive":3},{"url":"/dataset/santa-clara-reservoir-levels","name":"Santa Clara Reservoir Levels","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":49,"tagged_in_all":82,"items":[{"url":"/paper/pypots-a-python-toolbox-for-data-mining-on","title":"PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series","date":"2023-05-30","arxiv_id":"2305.18811","repositories_listed":5,"syntology":{"n":21,"n_ran":0,"n_unverified":21,"n_pointer_only":0}},{"url":"/paper/neural-controlled-differential-equations-for","title":"Neural Controlled Differential Equations for Irregular Time Series","date":"2020-05-18","arxiv_id":"2005.08926","repositories_listed":5,"syntology":{"n":20,"n_ran":4,"n_unverified":16,"n_pointer_only":2}},{"url":"/paper/on-neural-differential-equations","title":"On Neural Differential Equations","date":"2022-02-04","arxiv_id":"2202.02435","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/neural-cdes-for-long-time-series-via-the-log","title":"Neural Rough Differential Equations for Long Time Series","date":"2020-09-17","arxiv_id":"2009.08295","repositories_listed":4,"syntology":{"n":6,"n_ran":5,"n_unverified":1,"n_pointer_only":6}},{"url":"/paper/bridging-dynamic-factor-models-and-neural","title":"Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP","date":"2024-09-13","arxiv_id":"2409.08732","repositories_listed":2,"syntology":null},{"url":"/paper/synthcity-facilitating-innovative-use-cases","title":"Synthcity: facilitating innovative use cases of synthetic data in different data modalities","date":"2023-01-18","arxiv_id":"2301.07573","repositories_listed":2,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/learnable-path-in-neural-controlled","title":"Learnable Path in Neural Controlled Differential Equations","date":"2023-01-11","arxiv_id":"2301.04333","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_unverified":3,"n_pointer_only":6}},{"url":"/paper/neural-controlled-differential-equations-for-1","title":"Neural Controlled Differential Equations for Online Prediction Tasks","date":"2021-06-21","arxiv_id":"2106.11028","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/generalised-interpretable-shapelets-for","title":"Generalised Interpretable Shapelets for Irregular Time Series","date":"2020-05-28","arxiv_id":"2005.13948","repositories_listed":2,"syntology":{"n":5,"n_ran":0,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/path-imputation-strategies-for-signature","title":"Path Imputation Strategies for Signature Models of Irregular Time Series","date":"2020-05-25","arxiv_id":"2005.12359","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/a-kernel-based-approach-for-accurate-steady","title":"A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series","date":"2025-06-04","arxiv_id":"2506.04204","repositories_listed":1,"syntology":null},{"url":"/paper/pyrregular-a-unified-framework-for-irregular","title":"PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks","date":"2025-05-09","arxiv_id":"2505.06047","repositories_listed":1,"syntology":null},{"url":"/paper/can-multimodal-llms-perform-time-series","title":"Can Multimodal LLMs Perform Time Series Anomaly Detection?","date":"2025-02-25","arxiv_id":"2502.17812","repositories_listed":1,"syntology":null},{"url":"/paper/generative-pretrained-embedding-and","title":"Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition","date":"2024-12-27","arxiv_id":"2412.19732","repositories_listed":1,"syntology":null},{"url":"/paper/robust-real-time-mortality-prediction-in-the","title":"Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning","date":"2024-11-06","arxiv_id":"2411.04285","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-glucose-level-prediction-of-icu","title":"Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series","date":"2024-11-03","arxiv_id":"2411.01418","repositories_listed":1,"syntology":null},{"url":"/paper/flextsf-a-universal-forecasting-model-for","title":"FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities","date":"2024-10-30","arxiv_id":"2410.23160","repositories_listed":1,"syntology":null},{"url":"/paper/irregularity-informed-time-series-analysis","title":"Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics","date":"2024-10-16","arxiv_id":"2410.12257","repositories_listed":1,"syntology":null},{"url":"/paper/amortized-control-of-continuous-state-space","title":"Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series","date":"2024-10-08","arxiv_id":"2410.05602","repositories_listed":1,"syntology":null},{"url":"/paper/emit-event-based-masked-auto-encoding-for","title":"EMIT- Event-Based Masked Auto Encoding for Irregular Time Series","date":"2024-09-25","arxiv_id":"2409.16554","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/functional-latent-dynamics-for-irregularly","title":"Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting","date":"2024-05-06","arxiv_id":"2405.03582","repositories_listed":1,"syntology":null},{"url":"/paper/spatiotemporal-representation-learning-for","title":"Spatiotemporal Representation Learning for Short and Long Medical Image Time Series","date":"2024-03-12","arxiv_id":"2403.07513","repositories_listed":1,"syntology":null},{"url":"/paper/stable-neural-stochastic-differential","title":"Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data","date":"2024-02-22","arxiv_id":"2402.14989","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/contiformer-continuous-time-transformer-for-1","title":"ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling","date":"2024-02-16","arxiv_id":"2402.10635","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/probabilistic-forecasting-of-irregular-time","title":"Probabilistic Forecasting of Irregular Time Series via Conditional Flows","date":"2024-02-09","arxiv_id":"2402.06293","repositories_listed":1,"syntology":null},{"url":"/paper/invertible-solution-of-neural-differential","title":"DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis","date":"2024-01-10","arxiv_id":"2401.04979","repositories_listed":1,"syntology":null},{"url":"/paper/extended-deep-adaptive-input-normalization","title":"Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks","date":"2023-10-23","arxiv_id":"2310.14720","repositories_listed":1,"syntology":null},{"url":"/paper/generative-modeling-of-regular-and-irregular","title":"Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs","date":"2023-10-04","arxiv_id":"2310.02619","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_unverified":4,"n_pointer_only":10}},{"url":"/paper/continuous-time-evidential-distributions-for","title":"Continuous Time Evidential Distributions for Irregular Time Series","date":"2023-07-25","arxiv_id":"2307.13503","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/precursor-of-anomaly-detection-for-irregular","title":"Precursor-of-Anomaly Detection for Irregular Time Series","date":"2023-06-27","arxiv_id":"2306.15489","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}}],"syntology_records":15,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}