Irregular Time Series

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 49 papers with code (82 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
  30 May 2023 5 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.
• Neural Controlled Differential Equations for Irregular Time Series
  18 May 2020 5 repositories listed Syntology ran 4 of 20 samples · 16 unverified · 2 pointer-only (licence)
  The resulting \emph{neural controlled differential equation} model is directly applicable to the general setting of partially-observed irregularly-sampled multivariate time series, and (unlike previous work on this…
• On Neural Differential Equations
  4 Feb 2022 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  Topics include: neural ordinary differential equations (e.
• Neural Rough Differential Equations for Long Time Series
  17 Sep 2020 4 repositories listed Syntology ran 5 of 6 samples · 1 unverified · 6 pointer-only (licence)
  Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions…
• Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
  13 Sep 2024 2 repositories listed
  However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data.
• Synthcity: facilitating innovative use cases of synthetic data in different data modalities
  18 Jan 2023 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time…
• Learnable Path in Neural Controlled Differential Equations
  11 Jan 2023 2 repositories listed Syntology ran 3 of 6 samples · 3 unverified · 6 pointer-only (licence)
  Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing.
• Neural Controlled Differential Equations for Online Prediction Tasks
  21 Jun 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  This is fine when the whole time series is observed in advance, but means that Neural CDEs are not suitable for use in \textit{online prediction tasks}, where predictions need to be made in real-time: a major use case…
• Generalised Interpretable Shapelets for Irregular Time Series
  28 May 2020 2 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  The shapelet transform is a form of feature extraction for time series, in which a time series is described by its similarity to each of a collection of `shapelets'.
• Path Imputation Strategies for Signature Models of Irregular Time Series
  25 May 2020 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.
• A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
  4 Jun 2025 1 repository listed
  This paper addresses the challenge of accurately detecting the transition from the warmup phase to the steady state in performance metric time series, which is a critical step for effective benchmarking.
• PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
  9 May 2025 1 repository listed
  Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental…
• Can Multimodal LLMs Perform Time Series Anomaly Detection?
  25 Feb 2025 1 repository listed
  Starting with the univariate case (point- and range-wise anomalies), we extend our evaluation to more practical scenarios, including multivariate and irregular time series scenarios, and variate-wise anomalies.
• Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition
  27 Dec 2024 1 repository listed
  We delve into an architecture anchored on Transformer Decoder-based pre-trained embeddings, reminiscent of the GPT design, and contrast it with the previously established state-of-the-art (SOTA) ELMo embeddings for…
• Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning
  6 Nov 2024 1 repository listed
  In this study, we define a framework for applying TD learning to real-time irregularly sampled time series data using a Semi-Markov Reward Process.
• Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series
  3 Nov 2024 1 repository listed
  Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality.
• FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities
  30 Oct 2024 1 repository listed
  Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years.
• Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
  16 Oct 2024 1 repository listed
  We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS).
• Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series
  8 Oct 2024 1 repository listed
  Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling.
• EMIT- Event-Based Masked Auto Encoding for Irregular Time Series
  25 Sep 2024 1 repository listed Syntology ran 14 of 15 samples · 1 unverified
  Irregular time series, where data points are recorded at uneven intervals, are prevalent in healthcare settings, such as emergency wards where vital signs and laboratory results are captured at varying times.
• Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
  6 May 2024 1 repository listed
  In order to capture the continuous dynamics of the irregular time series, many models rely on solving an Ordinary Differential Equation (ODE) in the hidden state.
• Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
  12 Mar 2024 1 repository listed
  Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis.
• Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
  22 Feb 2024 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  Neural Stochastic Differential Equations (Neural SDEs) extend Neural ODEs by incorporating a diffusion term, although this addition is not trivial, particularly when addressing irregular intervals and missing values.
• ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
  16 Feb 2024 1 repository listed Syntology ran 8 of 12 samples · 4 unverified
  A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data.
• Probabilistic Forecasting of Irregular Time Series via Conditional Flows
  9 Feb 2024 1 repository listed
  Probabilistic forecasting of irregularly sampled multivariate time series with missing values is an important problem in many fields, including health care, astronomy, and climate.
• DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
  10 Jan 2024 1 repository listed
  Real-world time series analysis faces significant challenges when dealing with irregular and incomplete data.
• Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
  23 Oct 2023 1 repository listed
  Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency.
• Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
  4 Oct 2023 1 repository listed Syntology ran 6 of 10 samples · 4 unverified · 10 pointer-only (licence)
  In this work, we introduce Koopman VAE (KoVAE), a new generative framework that is based on a novel design for the model prior, and that can be optimized for either regular and irregular training data.
• Continuous Time Evidential Distributions for Irregular Time Series
  25 Jul 2023 1 repository listed Syntology ran 12 of 15 samples · 3 unverified
  Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from.
• Precursor-of-Anomaly Detection for Irregular Time Series
  27 Jun 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Unlike conventional anomaly detection, which focuses on determining whether a given time series observation is an anomaly or not, PoA detection aims to detect future anomalies before they happen.

Syntology lines on 15 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.