[D] Self-supervised/unsupervised approaches for time series?

What are self-supervised/unsupervised approaches for time series? I want to learn a lower-dimensional representation of the time series data (patterns/interactions between features & temporally) before I apply any supervised learning, for a forecasting application. It's not the usual approach, but I want to try.

I know that autoencoders work on tabular data. But what should I do for time series data where I'm feeding in \[batch\_size, sequence\_length, num\_features\], are there tools other than autoencoders, or autoencoders is fine?

application apply autoencoders data features forecasting interactions learn machinelearning patterns representation series supervised learning tabular tabular data time series unsupervised work