awslabs/gluonts

Add exogenous features to `statsforecast` model wrappers

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#2,410 创建于 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

贡献者指南

技术栈
python
领域
datamachine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
Pythontime series fundamentals
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
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.