Lasso performance, with the added benefit of providing multiple solutions.

Unlike traditional backdoor attacks that focus on specific class labels, our approach aims to induce poisoned models to predict future data as a predefined target pattern.

Accurately modeling the correlation structure of errors is critical for reliable uncertainty quantification in probabilistic time series forecasting.

Building upon Miller's framework of explanations derived from research in multiple

However, existing methods assume a reliable and unbiased forecasting environment, which isnot always available inthe wild. Inthis work, we investigate the vulnerability ofspatiotemporal trafficforecasting models andpropose apractical adversarial spatiotemporal attack framework.

When data gathered from thereal world ispolluted, the absence of global information will damage the robust prediction capability of these algorithms. To address these problems, we propose a novel frameworknamed GLAFF.Withinthisframework,thetimestamps aremodeled individually to capture the global dependencies.