fix pred_std scaling

[Debadri-das]

Describe your changes

Immediately after softplus, scale the predictor standard deviation (pred_std) using the one-step difference statistics (self.diff_std).

Instead of beginning around ~0.69, this first scales pred_std to the empirical step differences. This keeps measurements like NLL and CRPS from experiencing logarithmic loss explosions in the early stages of training. This offers a better initialization scale, as was discussed with @joeloskarsson. We consider this to be the main initialization step; training curve evaluation is necessary to determine whether the suggested / softplus(0.) scaling multiplier will be needed as a follow-up.
None.

Issue Link

linked to #347

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[Debadri-das]

Hi @sadamov @joeloskarsson , I noticed this issue regarding pred_std scaling and have created a preliminary PR adjusting the initialization to self.diff_std. Could you please review this?