What question did this study set out to answer?

The study aims to model information blackouts in time series data, specifically focusing on missing not-at-random scenarios.

January 6, 2026Open Access

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

Key Points

The study aims to model information blackouts in time series data, specifically focusing on missing not-at-random scenarios.
Evaluates imputation and forecasting for traffic sensor data
Compares MAR and MNAR modeling approaches
Uses Kalman filtering with RTS smoothing for MAR
Applies logistic missingness model for MNAR on datasets
Kalman filtering effectively estimates missing values
Imputation shows improved accuracy in traffic data
Post-blackout forecasting yields insights for future traffic modeling

Abstract

This preprint studies traffic-sensor blackouts (contiguous missing intervals) and evaluates two tasks: (1) imputation, i.e., reconstructing values inside blackout windows, and (2) post-blackout forecasting at horizons +1, +3, and +6 steps on a 5-minute grid. We compare a MAR linear dynamical system (Kalman filtering with RTS smoothing) against an MNAR extension that treats the missingness mask as an informative observation channel using a logistic missingness model conditioned on the latent state. The repository includes code, evaluation-window manifests, and notebooks for experiments on the Seattle Loop dataset and the METR-LA dataset.

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