Forecasting Irregularly Sampled Time Series using Graphs. (arXiv:2305.12932v2 [cs.LG] UPDATED)

Forecasting irregularly sampled time series with missing values is a crucial
task for numerous real-world applications such as healthcare, astronomy, and
climate sciences. State-of-the-art approaches to this problem rely on Ordinary
Differential Equations (ODEs) which are known to be slow and often require
additional features to handle missing values. To address this issue, we propose
a novel model using Graphs for Forecasting Irregularly Sampled Time Series with
missing values which we call GraFITi. GraFITi first converts the time series to …

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