[New model] DeepVAR

描述

贡献者指南

技术栈

python

领域

machine 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。

5

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。

over 1 week

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。

needs investigation

前置要求

Understanding of DeepAR modelFamiliarity with darts codebaseKnowledge of time series forecastingRead paper arXiv:1910.03002

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。

15

研究方向

This issue proposes implementing the DeepVAR model from the paper (arXiv:1910.03002) for multivariate probabilistic time series forecasting. The first step is to study the paper and the existing DeepAR implementation in darts (likely in 'darts/models/forecasting/rnn model.py') to determine if DeepVAR can be integrated into the existing RNNModel class or requires a new model. Review the repository's contributing guidelines and existing model structure to plan the implementation. No prior discussion or linked PRs exist, so the contributor should initiate a discussion on the approach before coding.