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To address these challenges, we propose CITRAS, a decoder-only Transformer that flexibly integrates multiple target variables, observed covariates, and kno","title":"CITRAS: Covariate-Informed Transformer for Time Series Forecasting","url":"https://arxiv.org/abs/2503.24007","vendor":"arxiv_cs_ai"},"summary":"arXiv:2503.24007v4 Announce Type: replace-cross \nAbstract: In time series forecasting, covariates represent external factors that influence target variables. Some covariates are observable only in the past (observed covariates, such as recorded weather data), while others are known in advance (known covariates, such as calendar events or discount schedules). Although covariates have the potential to enhance forecasting performance, most deep learning-based forecasting models struggle to address the length discrepancy between variables caused by the future portion of known covariates and fail to leverage them flexibly. 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