I'm using gaussian process regression with an RBF kernel to forecast a time series. I'm using
sci-kit learn, with a kernel:
1**2 * RBF(length_scale=1). Target values are normalized.
This does a great job of fitting to the time series training set, but on the unseen testing set, it consistently underestimates the actual values. Like this:
Are there parameters in an RBF kernel, or a custom kernel, that can be used to correct this under-estimation?
For example, could I utilize the
sci-kit learn to create a custom kernel which scales the
RBF kernel to the mean of the series. Like this:
ConstantKernel(constant_value=1) + 1**2 * RBF(length_scale=1)