Evaluating the Robustness of Zero-Shot Time Series Models Against Data Degradation in Industrial Sensor Streams

Zero-shot time series foundation models (TSFMs) forecast unseen series without target-specific training, offering a way to significantly reduce the deployment overhead in enterprise pipelines and industrial sensor streams. However, their performance is documented almost exclusively on pristine benchmarks, whereas industrial data streams feature missing values, sensor outages, and amplitude spikes. Whether these models absorb such faults natively, and thus whether costly imputation stages can be removed, remains unquantified. In this study, we evaluate the robustness of three TSFMs from distinct architectural families, Chronos (tokenization), TimesFM (patching), and TiRex (xLSTM), subjecting their context windows to random missing data, contiguous block missingness, and injection of point anomalies at escalating severities and assessing point accuracy (relative RMSE) and 80% interval reliability (PICP) against a clean baseline. The results show that random dropout and transient spikes are natively absorbed by all three models (accuracy loss below 3%), while sustained contiguous outages are the dominant failure mode, with patch-based TimesFM most exposed (up to 59%). TiRex stays well calibrated throughout, while tokenization-based Chronos yields persistently overconfident intervals. These patterns persist and the contiguous outage vulnerability is amplified on data with genuine operational anomalies. For the evaluated models, sensor streams, and tested corruption severities, the results indicate that direct inference remains close to the clean-context baseline under independent random loss and sparse positive spikes, whereas prolonged outages warrant additional missing-data handling.