unit8co/darts

mc_dropout with predict_likelihood_parameters

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#2105 opened on Dec 4, 2023

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Description

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.

Contributor guide

Tech stack
python
Domain
machine learningdata
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Familiarity with darts libraryUnderstanding of Monte Carlo DropoutUnderstanding of aleatoric vs epistemic uncertainty
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
35
Research direction
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.