Hi, new to this HF model.
I am using some basic nondescript stock data consisting of three columns: timestamp, price, and volume. I create a time series dataset by using the past 20 timestamps to predict the next two. See the below images for the full code, but here is the error:
File ~/opt/miniforge3/envs/temp/lib/python3.11/site-packages/torch/nn/modules/linear.py:114, in Linear.forward(self, input) 113 def forward(self, input: Tensor) -> Tensor: --> 114 return F.linear(input, self.weight, self.bias) RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x22 and 20x64)
I happen to think it is related to context_length and lags_sequence since if i change those around, the same error with different inside numbers differing by 2 appear (e.g. 19 & 17 rather than what it is now: 22 & 20).
I am passing in data of the shape
torch.Size([128, 20, 2])
torch.Size([128, 2, 2])
which I believe is correct per the documentation for univariate time series. This model would benefit greatly from a medium article or further documentation on how to use it. But for today’s issue, I do not believe my code is incorrect as shown below.
predictions = model( past_values=batch['past_values'], past_time_features=batch['past_time_features'], past_observed_mask=None, future_values=batch['future_values'], future_time_features=batch['future_time_features'], )
I’ll leave the rest of the photos in the comments.