Imagen de Portada

U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kuleshov, Ilya, Marusov, Alexander, Zaytsev, Alexey
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Parameter-Efficient Neural CDEs via Implicit Function Jacobians
por: Kuleshov, Ilya, et al.
Publicado: (2025)

A theoretical framework for self-supervised contrastive learning for continuous dependent data
por: Marusov, Alexander, et al.
Publicado: (2025)

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
por: Serov, Egor, et al.
Publicado: (2026)

DeNOTS: Stable Deep Neural ODEs for Time Series
por: Kuleshov, Ilya, et al.
Publicado: (2024)

Long-term drought prediction using deep neural networks based on geospatial weather data
por: Marusov, Alexander, et al.
Publicado: (2023)

Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
por: Klötergens, Christian, et al.
Publicado: (2026)

Probabilistic Forecasting of Irregular Time Series via Conditional Flows
por: Yalavarthi, Vijaya Krishna, et al.
Publicado: (2024)

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs
por: Klötergens, Christian, et al.
Publicado: (2025)

Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows
por: Yalavarthi, Vijaya Krishna, et al.
Publicado: (2024)

Calibrated Probabilistic Forecasts for Arbitrary Sequences
por: Marx, Charles, et al.
Publicado: (2024)

ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
por: Chen, Yuqi, et al.
Publicado: (2024)

Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series
por: Yalavarthi, Vijaya Krishna, et al.
Publicado: (2022)

Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
por: Li, Yijun, et al.
Publicado: (2023)

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting
por: Klötergens, Christian, et al.
Publicado: (2024)

An Adversarial Learning Approach to Irregular Time-Series Forecasting
por: Nam, Heejeong, et al.
Publicado: (2024)

Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
por: Wang, Chengsen, et al.
Publicado: (2025)

Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting
por: Zhou, Ziyu, et al.
Publicado: (2025)

Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
por: Kim, Byunghyun, et al.
Publicado: (2024)

Koopman Ensembles for Probabilistic Time Series Forecasting
por: Frion, Anthony, et al.
Publicado: (2024)

SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
por: Rezaei, Mohammad R., et al.
Publicado: (2026)

Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
por: Li, Boyuan, et al.
Publicado: (2026)

HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting
por: Li, Boyuan, et al.
Publicado: (2025)

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
por: Liu, Xvyuan, et al.
Publicado: (2025)

Uniting contrastive and generative learning for event sequences models
por: Yugay, Aleksandr, et al.
Publicado: (2024)

TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting
por: Liu, Zhipeng, et al.
Publicado: (2025)

Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
por: Qiu, Xiangfei, et al.
Publicado: (2026)

Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
por: Bazarova, Alexandra, et al.
Publicado: (2024)

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
por: Huk, David, et al.
Publicado: (2026)

Better Batch for Deep Probabilistic Time Series Forecasting
por: Zheng, Vincent Zhihao, et al.
Publicado: (2023)

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
por: Madhusudhanan, Kiran, et al.
Publicado: (2024)

Multivariate Probabilistic Time Series Forecasting with Correlated Errors
por: Zheng, Vincent Zhihao, et al.
Publicado: (2024)

BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
por: Ni, Zelin, et al.
Publicado: (2023)

Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
por: Koldasbayeva, Diana, et al.
Publicado: (2025)

ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
por: Liu, Xvyuan, et al.
Publicado: (2025)

STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting
por: Wang, Yulong, et al.
Publicado: (2025)

Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models
por: Kuskova, Valentina, et al.
Publicado: (2026)

WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
por: Stepikin, Alexander, et al.
Publicado: (2025)

Parallel-in-Time Probabilistic Numerical ODE Solvers
por: Bosch, Nathanael, et al.
Publicado: (2023)

SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
por: Zhang, Zhenwei, et al.
Publicado: (2023)

Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning
por: Kaufman, Ilya, et al.
Publicado: (2024)