Researchers have developed a novel factor graph approach to address the scalability challenges in multi-output Gaussian process regression. This new method expresses the regression problem as a factor graph, enabling efficient posterior computation through Gaussian message passing. The formulation scales effectively with the number of data samples and missing observations, outperforming traditional kernel-matrix and sparse-variational methods in terms of speed and accuracy, particularly for time series forecasting tasks. AI
IMPACT This new formulation offers a more scalable and accurate approach for complex regression tasks, potentially impacting fields like time series forecasting and scientific modeling.
RANK_REASON The cluster contains an academic paper detailing a new methodological approach to a machine learning problem.
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- arXiv
- electricity time series forecasting
- linear model of coregionalization
- Matérn processes
- electricity time series
- Hugging Face
- sparse-variational inducing-point
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