I'm testing your code with ETTh1 dataset using both multivariate predict univariate task (MS) and univariate predict univariate (S). If I understand the process correctly, MS is to forecast the future OT with an input of [feature1, feature2, ..., featureN, OT], while the S task is to forecast the future OT based on the observed OT. However, the MSE of the MS task is larger than that of the S task.
My question is, why is the performance worse with more features provided?
Hi, thank you for your work.
I'm testing your code with ETTh1 dataset using both multivariate predict univariate task (MS) and univariate predict univariate (S). If I understand the process correctly, MS is to forecast the future OT with an input of [feature1, feature2, ..., featureN, OT], while the S task is to forecast the future OT based on the observed OT. However, the MSE of the MS task is larger than that of the S task.
My question is, why is the performance worse with more features provided?
Thank you!