5 citations · 6 across the 10 of their papers we have counts for
10 papers
Dr-CiK: A Benchmark testing Deep Research for Context-Aided Forecasting
Yihong Tang, Andrew Robert Williams, Arjun Ashok +7
Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heteroge…
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok +4
Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods. Howe…
Drinking water contamination as a population-wide determinant of mortality in California
Kiley Kennedy, Vincent Zheng, Benjamin Q Huynh
Drinking water contamination, a known determinant of adverse health outcomes, remains widespread and inequitably distributed amidst aging infrastructure. Regulatory oversight is th…
Frequency-Constrained Learning for Long-Term Forecasting
Menglin Kong, Vincent Zhihao Zheng, Lijun Sun
Many real-world time series exhibit strong periodic structures arising from physical laws, human routines, or seasonal cycles. However, modern deep forecasting models often fail to…
Dynamic Modes as Time Representation for Spatiotemporal Forecasting
Menglin Kong, Vincent Zhihao Zheng, Xudong Wang +1
This paper introduces a data-driven time embedding method for modeling long-range seasonal dependencies in spatiotemporal forecasting tasks. The proposed approach employs Dynamic M…
Beyond Naïve Prompting: Strategies for Improved Context-aided Forecasting with LLMs
Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng +5
Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form. While large language models (LLMs) s…
MVG-CRPS: A Robust Loss Function for Multivariate Probabilistic Forecasting
Vincent Zhihao Zheng, Lijun Sun
Multivariate Gaussian (MVG) distributions are central to modeling correlated continuous variables in probabilistic forecasting. Neural forecasting models typically parameterize the…
Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Vincent Zhihao Zheng, Lijun Sun
Accurately modeling the correlation structure of errors is critical for reliable uncertainty quantification in probabilistic time series forecasting. While recent deep learning mod…
Better Batch for Deep Probabilistic Time Series Forecasting
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making. Howe…
Probabilistic Traffic Forecasting with Dynamic Regression
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
This paper proposes a dynamic regression (DR) framework that enhances existing deep spatiotemporal models by incorporating structured learning for the error process in traffic fore…