Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling
Abstract
Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps due to sensor malfunctions or high data acquisition costs. Existing approaches for IMTS either consider a two-stage impute-then-model framework or involve specialized architectures specific to a particular model and task. We perform a series of experiments to derive insights about the performance of IMTS methods on a variety of semi-synthetic and real-world datasets for both classification and forecasting. We also introduce Missing Feature-aware Time Series Modeling (MissTSM) or MissTSM, a simple model-agnostic and imputation-free approach for IMTS modeling. We show that MissTSM shows competitive performance compared to other IMTS approaches, especially when the amount of missing values is large and the data lacks simplistic periodic structures - conditions common to real-world IMTS applications.
Cite
Text
Neog et al. "Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling." Transactions on Machine Learning Research, 2026.Markdown
[Neog et al. "Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling." Transactions on Machine Learning Research, 2026.](https://mlanthology.org/tmlr/2026/neog2026tmlr-investigating/)BibTeX
@article{neog2026tmlr-investigating,
title = {{Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling}},
author = {Neog, Abhilash and Daw, Arka and Fatemi, Sepideh and Sawhney, Medha and Pradhan, Aanish and Lofton, Mary E. and McAfee, Bennett J. and Breef-Pilz, Adrienne and Wander, Heather L. and Howard, Dexter W and Carey, Cayelan C. and Hanson, Paul and Karpatne, Anuj},
journal = {Transactions on Machine Learning Research},
year = {2026},
url = {https://mlanthology.org/tmlr/2026/neog2026tmlr-investigating/}
}