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Observations made in continuous time are often irregular and contain themissing values across different channels. One approach to handle the missingdata is imputing it using splines, by fitting the piecewise polynomials to theobserved values. We propose using the splines as an input to a neural network,in particular, applying the transformations on the interpolating functiondirectly, instead of sampling the points on a grid. To do that, we design thelayers that can operate on splines and which are analogous to their discretecounterparts. This allows us to represent the irregular sequence compactly anduse this representation in the downstream tasks such as classification andforecasting. Our model offers competitive performance compared to the existingmethods both in terms of the accuracy and computation efficiency.

Irregularly-Sampled Time Series Modeling with Spline Networks