Papers Irregular Time Series
“Irregular Time Series” 태그가 달린 논문 82편 · 필터 해제
Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series
Time series data in real-world applications such as healthcare, climate modeling, and finance are often irregular, multimodal, and messy, with varying sampling rates, asynchronous modalities, and pervasive missingness. H…
Irregular Time SeriesTime SeriesTime Series AnalysisRobust Moment Identification for Nonlinear PDEs via a Neural ODE Approach
We propose a data-driven framework for learning reduced-order moment dynamics from PDE-governed systems using Neural ODEs. In contrast to derivative-based methods like SINDy, which necessitate densely sampled data and ar…
Irregular Time SeriesA Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
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. The goal is to i…
BenchmarkingIrregular Time SeriesTime SeriesRethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging …
AstronomyIrregular Time SeriesTime SeriesTime Series ForecastingPYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks
Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental sci…
Irregular Time SeriesMissing ValuesTime SeriesTime Series ClassificationCan Multimodal LLMs Perform Time Series Anomaly Detection?
Large language models (LLMs) have been increasingly used in time series analysis. However, the potential of multimodal LLMs (MLLMs), particularly vision-language models, for time series remains largely under-explored. On…
Anomaly DetectionIrregular Time SeriesTime SeriesTime Series Analysis+1Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition
Within the evolving landscape of smart homes, the precise recognition of daily living activities using ambient sensor data stands paramount. This paper not only aims to bolster existing algorithms by evaluating two disti…
Activity RecognitionDecoderIrregular Time SeriesTime SeriesAn Adversarial Learning Approach to Irregular Time-Series Forecasting
Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditio…
Irregular Time SeriesTime SeriesTime Series ForecastingRobust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning
The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to t…
Irregular Time SeriesMortality PredictionTime SeriesEnhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series
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. This study prese…
Data IntegrationFeature EngineeringIrregular Time SeriesMortality Prediction+2FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities
Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years. Existing works typically assume regularly sampled, well-structured data, limiting thei…
Irregular Time SeriesMissing ValuesTime SeriesTime Series ForecastingIrregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregula…
Irregular Time SeriesTime SeriesTime Series AnalysisAmortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series
Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we propose the Amortized Control of continu…
Computational EfficiencyIrregular Time SeriesRepresentation LearningTime Series+1TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
In many domains, such as healthcare, time-series data is often irregularly sampled with varying intervals between observations. This poses challenges for classical time-series models that require equally spaced data. To …
Irregular Time SeriesPhenotype classificationRepresentation LearningTime Series+1Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
Irregularly sampled time series forecasting, characterized by non-uniform intervals, is prevalent in practical applications. However, previous research have been focused on regular time series forecasting, typically rely…
Irregular Time SeriesTime SeriesTime Series ForecastingEMIT- Event-Based Masked Auto Encoding for Irregular Time Series
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. This variab…
Decision MakingIrregular Time SeriesSelf-Supervised LearningTime SeriesBridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP
Gross domestic product (GDP) nowcasting is crucial for policy-making as GDP growth is a key indicator of economic conditions. Dynamic factor models (DFMs) have been widely adopted by government agencies for GDP nowcastin…
Irregular Time SeriesDeNOTS: Stable Deep Neural ODEs for Time Series
Neural CDEs provide a natural way to process the temporal evolution of irregular time series. The number of function evaluations (NFE) is these systems' natural analog of depth (the number of layers in traditional neural…
Irregular Time SeriesState Space ModelsTime SeriesMarginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows
Probabilistic forecasting models for joint distributions of targets in irregular time series with missing values are a heavily under-researched area in machine learning, with, to the best of our knowledge, only two Model…
Gaussian ProcessesGPRIrregular Time SeriesMissing Values+2Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
Irregularly sampled time series with missing values are often observed in multiple real-world applications such as healthcare, climate and astronomy. They pose a significant challenge to standard deep learning models tha…
AstronomyIrregular Time SeriesMissing ValuesMultivariate Time Series Forecasting+2