From Reconstruction to Imputation: Test-Time Optimization for Missing Time-Series Channels

The paper proposes a test‑time optimization technique that converts a frozen sequence reconstructor into an imputer for missing time‑series channels. By treating missing entries as optimization variables, clamping observed data, and backpropagating to minimize reconstruction error only on observed coordinates, the method avoids the need for artificial mask augmentation or full‑channel dropout during training. Experiments on five real‑world datasets show that this refinement improves mean absolute error in most dataset‑backbone combinations and can outperform mask‑aware imputers on some datasets, though it requires more inference time and depends on cross‑channel coupling.

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