DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting 事件

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting arXiv:2601.21726v2 Announce Type: replace Abstract: Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification, failing to balance effectiveness and efficiency. In this paper, we introduce DropoutTS, a model-agnostic plugin that shifts the paradigm from "what" to learn to "how much" to learn. DropoutTS empl