AifuHan

I got the same result as you, 0.0223±0.0015

> I think the issue is the diffusion models i am currently using, namely diffusers vs originally i had my own denoising diffusion implementation. I can try to revert the...

> @hanlaoshi i copied over my traffic notebook here https://github.com/zalandoresearch/pytorch-ts/blob/version-0.7.0/examples/Traffic.ipynb but it needs to be fixed for the new API where there is no need to explicitly set the size...

cd node_modules/puppeteer npm install

> @hanlaoshi dlinear is an inherently univariate model so there is no need to group the time series etc. just pass the time series individually and predict individually like other...

> oh i see now you are not using those imports... so the issue seems to be in the dataset preparation helpers you have rather than dlinear... can you confirm...

> it doesnt make sense for dlinear to be multivariate, you would need to rehape the context_length, multivariate_dim tensor to context_length*multivariate_dim vector and then the nn.linear would be potentially huge:...

> also note that compraring univariate models against multivariate models on the smallish sized open datasets will always result in the univariate methods performing better due to the difficulty of...

> so Crps needs many prediction samples (so 1 more tensor dim) and one ground-truth sample so the shape of the prediction has an extra dim You're right. I remember...

> yes that is right I really appreciate you taking the time to explain the model to me clearly and concisely. It's impressive how quickly you understood my question and...