Modeling Information Blackouts in Missing Not-At-Random Time Series Data

arXiv:2601.01480 · stat.ML, cs.LG, stat.AP · Submitted 2026-01-04 · Read on arXiv

stat.ML, cs.LG, stat.AP

Submitted: 2026-01-04

Updated: 2026-09-01

Comments: 16 pages, 5 figures, 9 tables. Revised and substantially expanded version for AALTD 2026 (ECML-PKDD Workshop)

Code: https://github.com/BlackoutBayes/Modeling-Information-Blackouts-in-MNAR-Time-Series

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Extended Kalman Filter — 5× in this paper
• Latent Dynamics — 4× in this paper · explained in 2 episodes
• Missing Not At Random — 4× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Missing At Random — 1× in this paper

Sources

• BRITS: Bidirectional Recurrent Imputation for Time Series

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