LiPFormer combines a simplified patch-wise Transformer with a CLIP-style contrastive module that exploits future covariates to forecast time series more accurately and efficiently.
Mdtp: A multi-source deep traffic prediction framework over spatio-temporal trajectory data,
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Towards Lightweight Time Series Forecasting: a Patch-wise Transformer with Weak Data Enriching
LiPFormer combines a simplified patch-wise Transformer with a CLIP-style contrastive module that exploits future covariates to forecast time series more accurately and efficiently.