Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline

Understand

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.