Hey sorry I’m new to the forums and never recieved a notification when you replied.

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Are you saying that a model like a sequence to sequence would use the labels of day 1 and day 2??

Yep I believe you have understood correctly. Consider my earlier suggestion of using MSE loss for a vector of labels against a vector of outputs. An LSTM with T inputs will give T outputs. You have as many labels as you have input vectors (also T). Instead of taking only the final LSTM output at time T, you could take all of the outputs, pass them individually through a fully-connected layer to reduce dimensionality, and then compute MSE between the vector of outputs and the vector of labels.
More sophisticated sequence to sequence models such as encoder-decoder models are more well-suited to machine translation tasks, where the output sequence could differ in length to the input sequence. In your case both are length T.