awslabs/gluonts

Add exogenous features to `statsforecast` model wrappers

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#2,410 opened on Nov 2, 2022

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enhancementgood first issue

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Description

Description

The wrapper for statsforecast models added in #2360 does not use any exogenous features (only the past target is passed on to the model). It would be good to allow other standard fields from data entries, like at least feat_dynamic_real (or the past_ one) and feat_static_real, to be used.

Reference

See statsforecast's documentation on exogenous features: https://nixtla.github.io/statsforecast/examples/exogenous.html

Contributor guide

Research direction
Review the statsforecast documentation on exogenous features and the existing wrapper code in gluonts, then modify the wrapper to pass exogenous features to the statsforecast model.
Tech stack
python
Domain
machine learning
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.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGitstatsforecast
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65