Irregularly-Sampled Time Series Modeling with Spline Networks
Observations made in continuous time are often irregular and contain the missing values across different channels. One approach to handle the missing data is imputing it using splines, by fitting the piecewise polynomials to the observed values. We propose using the splines as an input to a neural n...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
19.10.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Observations made in continuous time are often irregular and contain the
missing values across different channels. One approach to handle the missing
data is imputing it using splines, by fitting the piecewise polynomials to the
observed values. We propose using the splines as an input to a neural network,
in particular, applying the transformations on the interpolating function
directly, instead of sampling the points on a grid. To do that, we design the
layers that can operate on splines and which are analogous to their discrete
counterparts. This allows us to represent the irregular sequence compactly and
use this representation in the downstream tasks such as classification and
forecasting. Our model offers competitive performance compared to the existing
methods both in terms of the accuracy and computation efficiency. |
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DOI: | 10.48550/arxiv.2210.10630 |