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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Missing ValuesTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth hidden states between observation inter…
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, and health. Such data represent fundamental challenges to many classical models fro…
AstronomyBIG-bench Machine LearningSurveyTime Series+1Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
Irregularly sampled time series are increasingly prevalent, particularly in medical domains. While various specialized methods have been developed to handle these irregularities, effectively modeling their complex dynami…
image-classificationImage ClassificationTime SeriesTime Series ClassificationLearning from Irregularly-Sampled Time Series: A Missing Data Perspective
Irregularly-sampled time series occur in many domains including healthcare. They can be challenging to model because they do not naturally yield a fixed-dimensional representation as required by many standard machine lea…
DecoderTime SeriesTime Series AnalysisNo Imputation Needed: A Switch Approach to Irregularly Sampled Time Series
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values. Most existing methods focus on handling ISTS by converting irregularly sampled data into regularly sampled data via imputation. Th…
ImputationMissing ValuesTime Series