PolarFormer: Radial-Angular Latent Modeling for Unconditional Time Series Generation
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...