Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

2026-07-30

AI Summary

This work addresses the challenge of modeling irregular multivariate time series with asynchronous observations and non-uniform sampling. Existing approaches either lose continuous-time semantics through discretization or incur high computational costs via ordinary differential equation solvers. To overcome these limitations, the authors propose WrapFlow, a novel framework that introduces continuous-time tokenization and a gap-aware mechanism to directly encode raw events while explicitly modeling unobserved intervals. WrapFlow leverages a standard Transformer to capture long-range dependencies and employs a residual flow matching training paradigm that avoids numerical simulation, enabling efficient continuous-time prediction. Evaluated on multiple real-world datasets, WrapFlow achieves state-of-the-art performance, generating high-quality continuous forecasts with only a small fixed number of rollback steps.

Abstract

Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenges stem from asynchronous observations, non-uniform sampling intervals, and the fact that temporal patterns themselves carry critical dynamic information. Existing approaches either rely on discretization-based preprocessing (e.g., interpolation, imputation, or aggregation), which disrupts the underlying continuous-time semantics, or adopt continuous-time modeling via ODE-based frameworks, which typically require specialized architectures and incur substantial computational overhead due to numerical solvers. To address these limitations, we propose WrapFlow, a continuous-time modeling framework for irregular time series forecasting. On the input side, WrapFlow introduces Continuous-Time Tokenization, which directly encodes raw observation events and explicitly models long unobserved intervals via gap-aware tokens. The resulting continuous-time tokens are then processed by a standard Transformer backbone to capture long-range temporal dependencies. On the output side, we develop a simulation-free training paradigm for Residual Flow Matching, which learns conditional residual vector fields around base predictions while avoiding numerical-solver simulation and backpropagation during training. This design enables high-quality continuous forecasting using only a small number of fixed rollout steps at inference. Extensive experiments on multiple real-world datasets demonstrate that WrapFlow achieves state-of-the-art performance.

Problem

• irregular time series
• continuous-time modeling
• asynchronous observations
• non-uniform sampling
• temporal forecasting

Innovation

• Continuous-Time Modeling
• Irregular Time Series
• Transformer
• Residual Flow Matching
• Gap-Aware Tokenization