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good first issuenew model
Description
https://arxiv.org/abs/1910.03002
TBD if we can make it part of our current RNNModel (DeepAR) or if we need a new model.
Contributor guide
- Tech stack
- python
- Domain
- machine learning
- Issue type
- feature
- DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
- 5
- Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
- over 1 week
- Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
- stale
- ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
- needs investigation
- Prerequisites
- Understanding of DeepAR modelFamiliarity with darts codebaseKnowledge of time series forecastingRead paper arXiv:1910.03002
- Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
- 15
- Research direction
- 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.