5 citations · 13 across the 16 of their papers we have counts for
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Into the ORBIT for Time Series: Training Regimes for Foundation Models
Hongjie Xia, Yiding Liu, Yifan Hu +2
Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored.…
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Yifan Hu, Hongzhou Chen, Peiyuan Liu +3
Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, making reliable forecasting fundamentally c…
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
Yiding Liu, Yifan Hu, Hongjie Xia +5
Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining. However, most existing TSFMs remain univariate, and re…
Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting
Yixin Wang, Yifan Hu, Peiyuan Liu +3
Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends. While recent advances have attempted t…
CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
Yaohua Zha, Chunlin Fan, Peiyuan Liu +4
Multi-channel time-series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models…