Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting

arXiv:2405.03582v1 Announce Type: new
Abstract: 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 learn- ing models that operate only on fully observed and regularly sampled time series. In order to capture the continuous dynamics of the irreg- ular time series, many models rely on solving an Ordinary Differential Equation (ODE) in the hidden state. These ODE-based models tend to perform …

abstract applications arxiv astronomy challenge climate cs.lg dynamics forecasting functional healthcare ing learn missing values multiple series standard time series time series forecasting type values world