1 citations · 2 across the 10 of their papers we have counts for
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HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
Yihan Wang, Annan Yu, Lujun Zhang +2
Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on…
Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
Annan Yu, Danielle C. Maddix, Boran Han +7
Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is stron…
Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
Annan Yu, Danielle C. Maddix, Boran Han +7
Transformers are widely used across data modalities, and yet the principles distilled from text models often transfer imperfectly to models trained to other modalities. In this pap…
Block-Biased Mamba for Long-Range Sequence Processing
Annan Yu, N. Benjamin Erichson
Mamba extends earlier state space models (SSMs) by introducing input-dependent dynamics, and has demonstrated strong empirical performance across a range of domains, including lang…
A Deep State Space Model for Rainfall-Runoff Simulations
Yihan Wang, Lujun Zhang, Annan Yu +2
The classical way of studying the rainfall-runoff processes in the water cycle relies on conceptual or physically-based hydrologic models. Deep learning (DL) has recently emerged a…