Masking the Gaps: An Imputation-Free Approach to Time Series Modeling with Missing Data

Abhilash Neog, Arka Daw, Sepideh Fatemi Khorasgani, Aanish Pradhan, Mary E. Lofton, Bennett J. McAfee, Adrienne Breef-Pilz, Heather L. Wander, Dexter W Howard, Cayelan Carey, Paul Hanson, Anuj Karpatne

Keywords: Time-Series, Deep Learning, Masked Autoencoders, Missing Data, Data Imputation

TL;DR: We propose a novel imputation-free approach of handling missing values in time series that can be trained in an end-to-end manner.

Abstract: Modeling time series is important in a variety of domains, yet it is challenged by the presence of missing values in real-world time-series datasets. Traditional frameworks for modeling time-series with missing values typically involve a two-step process, where the missing values are first filled-in using some imputation technique, followed by a time-series modeling approach on the imputed time-series. However, existing two-stage approaches suffer from two major drawbacks: first, the propagation of imputation errors into subsequent time-series modeling performance, and second, the inherent trade-offs between imputation efficacy and imputation complexity. To this end, we propose a novel imputation-free approach for handling missing values in time series termed {Miss}ing Feature-aware {T}ime {S}eries {M}odeling ({MissTSM}) with two main innovations. {First}, we develop a novel embedding scheme that treats every combination of time-step and feature (or channel) as a distinct token, encoding them into a high-dimensional space. {Second}, we introduce a novel {Missing Feature-Aware Attention (MFAA) Layer} to learn latent representations at every time-step based on partially observed features. We evaluate the effectiveness of MissTSM in handling missing values over multiple benchmark datasets using two synthetic masking techniques: masking completely at random (MCAR) and periodic masking, and a real-world missing-value dataset.

Primary Area: learning on time series and dynamical systems

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Submission Number: 12015