Author

Hao Xue

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#artificial intelligence Jun 2026

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

This work proposes UPLOTS, a Unified, Prompt-guided Language model framework that leverages a single pre-trained transformer backbone guided by learned constraint prompts, enabling on-demand generation with precise pattern control in time-series generation.

Du Yin, Hao Xue, Jin-Liang Deng et al. · arXiv.org · 0 citations

#artificial intelligence Jul 2026

TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting

TopoBrick is presented, a training-free framework for zero-shot building IoT (Internet-of-Things) forecasting that outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models.

Xiachong Lin, Du Yin, Arian Prabowo et al. · arXiv.org · 0 citations

Jul 2026

StrideDiffusion: Accelerating Diffusion Models for Time-series Generation

Results show that spectral evolution provides a practical and principled signal for fast time-series diffusion sampling, and a training-free spectral-aware sampler that adaptively selects the denoising stride from band-level activity.

Du Yin, Estrid He, Julián Jerónimo Bañuelos et al. · arXiv.org · 0 citations

Preprint Aug 2026

AsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting

This work proposes AsyTO, an Asymmetric Temporal Operator, an Asymmetric Temporal Operator that factorizes the tensor of per-variable operators into shared but distinct history-reading and future-writing temporal modes with per-variable mode-wise gains, complemented by a low-rank periodic prototype and a cycle-separabl...

Xiachong Lin, Du Yin, Hao Xue et al. · 0 citations

#artificial intelligence Jul 2026

A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks

A2TTA is proposed, an Anchored-and-Agile Test-Time Adaptation framework for evolving traffic sensor networks, which transforms topology-induced forecasting errors into an expandable output calibration problem and separates tem- poral adaptation into persistent global correction and agile context-specific specialization...

Du Yin, Xiachong Lin, Yuejie Tan et al. · arXiv.org · 0 citations

#machine learning Preprint Aug 2026

Multivariate Time Series Forecasting needs Cross Variable Loss

This work proposes CvLoss, a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph and shows that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones.

Kuiye Ding, Yifan Hu, Hanchen Wang et al. · 1 citation