Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
[Submitted on 16 May 2025 (v1), last revised 17 Nov 2025 (this version, v4)] / arXiv.org
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 due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these c | -- |