awslabs/gluonts

Using dynamic categorical features in NN models

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#392 创建于 2019年10月15日

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 (6 评论) (2 反应) (0 负责人)Python (3,888 star) (753 fork)batch import
enhancementgood first issuehelp wanted

描述

GluonTS allows to work with dynamic categorical features, but there is no way to use them in most of the NN models (e.g. DeepAR, Transformer, etc.)

They can be incorporated by applying embeddings and concatenating them with other dynamic features. Does it make sense to modify DeepAR and other models with this change, or is it better to stick with the current implementation and to write new model implementations that will use dynamic categorical features?

贡献者指南

技术栈
pythonpytorch
领域
machine learningdata
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。
mostly clear
前置要求
GluonTS basicsNeural network fundamentalsEmbedding layers
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。
40
研究方向
Investigate how dynamic categorical features are currently handled in GluonTS (e.g., in DeepAR and Transformer models). Propose adding an embedding layer for categorical features and concatenating them with existing dynamic features. Discuss with maintainers whether to modify existing models or create new ones. Review the model code in src/gluonts/model/ for relevant files.