In Gaussian process regression for time series forecasting, all observations are assumed to have the same noise. When this assumption does not hold, the forecasting accuracy degrades. Student’s t-processes handle time series with varying noise better than Gaussian processes, but may be less convenient in applications. In this article, we introduce a weighted noise kernel […]
Gaussian Process Regression With Varying Noise
