github code seems to predict flat lines only

[wassname]

Thanks for sharing, this series of timeseries models are really interesting. I especially like deeptime which works well for me (and I've tried adding multivariate, past only, inputs).

I particularly like the fact that you've testing on challenging multivariate weather and financial data. Many timeseries papers skip these difficult domains in favor of trivial problems. That's why I <3 Deeptime and the *Former papers.

I do have a question. I can't get ETSFormer to work. It seems like the current code mainly just predicts level, perhaps there is a bug in the uploaded code?

To replicate this I used a notebook and no substantial modifications. And you can see it's not predicting nice smooth ARIMA-like lines like in the paper. Instead it seems like it's all level with a tiny bit of growth in the first few steps. This happens at multiple lr's and with multiple datasets.

Am I missing something. Any ideas why this might be?

https://github.com/wassname/ETSformer/blob/w_notebook/notebook/run.ipynb

[wassname]

It's working in this codebase, which was used to produce a ets-former benchmark in a recent openreviewpaper.

[LotuSrc]

I find out that this problem will occur when K = 0. When K = 1, the curve looks better.

[YHU11]

Thanks for sharing, this series of timeseries models are really interesting. I especially like deeptime which works well for me (and I've tried adding multivariate, past only, inputs).感谢分享，这一系列的时间序列模型真的很有意思。我特别喜欢 deeptime，它对我来说效果很好（我尝试添加多变量、仅过去的输入）。

I particularly like the fact that you've testing on challenging multivariate weather and financial data. Many timeseries papers skip these difficult domains in favor of trivial problems. That's why I <3 Deeptime and the Former papers.我特别喜欢你对具有挑战性的多变量天气和财务数据进行测试的事实。许多时间序列论文跳过了这些困难的领域，转而关注琐碎的问题。这就是为什么我<3 Deeptime和以前的论文。

I do have a question. I can't get ETSFormer to work. It seems like the current code mainly just predicts level, perhaps there is a bug in the uploaded code?我有一个问题。我无法让 ETSFormer 工作。似乎现在的代码主要只是预测水平，也许上传的代码有bug？

To replicate this I used a notebook and no substantial modifications. And you can see it's not predicting nice smooth ARIMA-like lines like in the paper. Instead it seems like it's all level with a tiny bit of growth in the first few steps. This happens at multiple lr's and with multiple datasets.为了复制这一点，我使用了笔记本，没有进行实质性的修改。你可以看到它并没有像论文中那样预测出漂亮、光滑的类似 ARIMA 的线条。相反，在前几步中，似乎一切都处于水平，只有一点点增长。这发生在多个 LR 和多个数据集上。

Am I missing something. Any ideas why this might be?我错过了什么吗？有什么想法为什么会这样吗？

https://github.com/wassname/ETSformer/blob/w_notebook/notebook/run.ipynb

Hello, I have also been researching time series models for some time. Do you mean 'deep time' in this paper, Learning Deep Time Index Models for Time Series Forecasting