mc_dropout with predict_likelihood_parameters · unit8co/darts#2105

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説明

Hi, thanks for the prompt response regarding issue #2097 . I'm now able to utilize mc_dropout in historical_forecasts.

My current objective is to incorporate mc_dropout for obtaining epistemic uncertainty and predict_likelihood_parameters for acquiring aleatoric uncertainty. However, when setting predict_likelihood_parameters to True, I encounter an issue where I'm unable to use the sample method. Consequently, I'm limited to obtaining only one result with each call to historical_forecasts with predict_likelihood_parameters=True and mc_dropout=True.

I suspect that the solution might involve distinguishing the sample generation process by using mc_dropout and sample generation process based on the fitted distribution. Now they are using the same num_samples parameter.

コントリビューターガイド

技術スタック: python
領域: machine learningdata
Issue 種別: feature
難度: 3
推定時間: 1-2 days
活動状況: fresh
明確さ: mostly clear
前提条件: Familiarity with darts libraryUnderstanding of Monte Carlo DropoutUnderstanding of aleatoric vs epistemic uncertainty
初心者向け度: 35
調査方針: Investigate the implementation of `historical forecasts` in darts, specifically how `num samples` is used for both `mc dropout` and `predict likelihood parameters`. Examine the sampling logic to separate the two processes. Check the related issue #2097 for context. Consider modifying the function to allow different sample counts for each uncertainty source.