Comments (4)
WenWeiTHU
commented on August 27, 2024
3
Of course, we are in process now! You may be interested in our recent work where iTransformer is further enhanced with covariates for time series forecasting!
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
The code will also be released soon, please stay tuned!
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GuYith
commented on August 27, 2024
1
I have a similar question, I want to use covariates in training. Does the iTransformer support dynamic covariates? If so, could you provide some guidance or examples on how to implement this?
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royzjq
commented on August 27, 2024
Of course, we are in process now! You may be interested in our recent work where iTransformer is further enhanced with covariates for time series forecasting!
TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
The code will also be released soon, please stay tuned!
四个月了，呜呜呜
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WenWeiTHU
commented on August 27, 2024
大家好，因为文章投稿的问题，TimeXer的代码还没有开源，后续开源后会尽快通知感兴趣的朋友们
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Related Issues (20)
- what is the 'label_len' in dataloader for? HOT 2
- Mixed frequency forecast with iTransformer?
- When will the TimeXer code be released?
- 关于模型示意图的问题 HOT 2
- got killed HOT 1
- Multi-Label Prediction HOT 3
- 代码疑问 exp_long_term_forecasting.py HOT 1
- Why the results of PatchTST in iTransformer paper are worse than those in PatchTST paper? HOT 3
- 代码问题 HOT 5
- 关于变量关联分析图的问题 HOT 3
- Encoder-Decoder Architecture Issues HOT 3
- 预测曲线问题 HOT 3
- 尝试复现和改进时结果出现问题 HOT 2
- ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,5) HOT 2
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- 交通数据复现 HOT 1
- 自回归任务问题的请教 HOT 2
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