TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting
Time Series Forecasting (TSF) is highly vulnerable to backdoor attacks, yet effective defenses remain underexplored due to challenges arising from data entanglement and shifts in task formulation. To fill this gap, we conduct a systematic evaluation of thirteen representative backdoor defenses across the TSF life cycle and analyze their failure modes. Our results reveal two fundamental issues: (1) data entanglement induces channel-level signal dilution, rendering sample-filtering and trigger-synthesis defenses ineffective at localizing backdoors; and (2) task-formulation shift leads to training-loss degeneration, causing poisoned and clean windows to become indistinguishable at training s...
| Authors | Quang Duc Nguyen, Siyuan Liang, Yiming Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 May 2026 |
| Last updated at source | 25 May 2026 |
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