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This work proposes WorldTS, a world-modeling based forecasting framework that integrates multimodal covariates directly into the forecasting to further improve forecasting performance and employs a two-stage training strategy.
This work proposes PolarFormer, a novel framework for time series generation that addresses limitations through a polar decomposition-based discrete representation, and proposes a structurally decoupled generation strategy that models radial and angular token sequences jointly while leveraging their orthogonality to su...
Time series generation is essential for data augmentation and privacy-preserving analysis across many real-world domains. Recent progress in discrete token modeling~(DTM) has demonstrated strong potential by transforming continuous sequences into discrete representations and performing generation in the latent space. H...
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