unit8co/darts

[Feature] Improve handling & documentation of encoders and categorical static covariates in .fit_from_dataset()

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#3006 opened on Feb 4, 2026

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 (3 comments) (1 reaction) (0 assignees)Python (6,832 stars) (762 forks)batch import
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Description

Describe the issue linked to the documentation I couldn't find a mention of this in docs but it appears that TorchForecastingModel doesn't use encoders when using fit/predict_from_dataset - it doesn't warn about it either during the call. This seems like a potentially big blind side for the user since it is a silent behavior. Since this method is pretty well documented otherwise, this seems like an unexpected caveat.

I wonder if this should be documented OR actually move encoder logic to be included in the _from_dataset method calls.

Sidenote: This leads to issues with TFTModel since it just skips training and errors on predict if datasets don't contain future_covariates.

Sidenote2: categorical feature embeddings with TFTModel are also not working as expected (essentially ignored AFAIK) when using the _from_dataset methods since the code uses feature integer index instead of names for lookups but this seems like a completely separate issue.

Contributor guide

Tech stack
pythonpytorch
Domain
apimachine learningdocumentation
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.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Familiarity with dartsBasic PyTorchStatic covariates concept
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30
Research direction
Investigate how encoders are currently excluded from fit from dataset() in TorchForecastingModel by examining the source code in darts/models/forecasting/torch forecasting model.py. Identify where encoder logic should be integrated to support static covariates. Also analyze how categorical feature embeddings are handled in TFTModel to fix the feature integer index vs name mismatch. Review any comments or linked PRs in the issue for additional context.