Kuiye Ding — accepted AI-conference papers

2026

DISTILLING TIME SERIES FOUNDATION MODELS FOR EFFICIENT FORECASTING

Yuqi Li, Kuiye Ding, Szu-Yu Chen, Yingli Tian

ICASSP 2026poster

Time Series foundation models (TSFMs) deliver strong forecasting performance through large-scale pretraining, but their large parameter sizes make deployment costly. While knowledge distillation offers a natural and effective approach for model compression, techniques developed for general machine l…

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2026

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

Kuiye Ding, Fanda Fan, Chunyi Hou, Zheya Wang, Lei Wang, Zhengxin Yang +1

AAAI 2026technical

Multivariate time series forecasting is essential in domains such as finance, transportation, climate, and energy. However, existing patch-based methods typically adopt fixed-length segmentation, overlooking the heterogeneity of local temporal dynamics and the decoding heterogeneity of forecasting.

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