awslabs/gluonts

Add exogenous features to `statsforecast` model wrappers

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#2,410 建立於 2022年11月2日

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enhancementgood first issue

描述

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

貢獻者指南

技術棧
python
領域
datamachine learning
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-2 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
Pythontime series fundamentals
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
50
研究方向
Examine the existing statsforecast wrapper implementation added in PR #2360 to understand how the model wrapper currently works. Identify the specific wrapper class and its method that passes data to the model. Then, modify that method to accept additional arguments for exogenous features like feat dynamic real and feat static real, following the statsforecast documentation at https://nixtla.github.io/statsforecast/examples/exogenous.html. Ensure compatibility with the existing data structure.