awslabs/gluonts

Using dynamic categorical features in NN models

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#392 建立於 2019年10月15日

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 (6 留言) (2 反應) (0 負責人)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?

貢獻者指南

技術棧
pythonpytorch
領域
machine learningdata
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。
mostly clear
前置要求
GluonTS basicsNeural network fundamentalsEmbedding layers
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。
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.