Should forecasting with neural networks only be treated as a supervised learning (regression) problem?

I have recently made a work about the application of neural networks to time series forecasting, and I treated this as a supervised learning (regression) problem. I have come across the suggestion of treating this problem as an unsupervised, semi-supervised, or reinforcement learning problem. The ones that made this suggestion didn't know how to explain this approach and I haven't found any paper of this. So I found myself now trying to figure it out without any success. To my understanding:

Unsupervised learning problems (clustering and segmentation reduction) and semi-supervised learning problems (semi-supervised clustering and semi-supervised classification) can be used to decompose the time series but not forecast it.

Reinforcement learning problems (model-based and non-model-based on/off-policy) is to decision taken problems, not to forecast.

It is possible to treat forecasting time series with neural networks as an unsupervised, semi-supervised, or reinforcement learning problem? How it is done?

Concerning unsupervised learning, you could cluster data points (training samples) with respect to how some value(s) of interest changed $$t$$ time steps in the future (after having observed the training sample). Then, you could associate clusters with rough forecast-estimates. After all, you would treat the forecast value as a label associated with data points. Afterwards, you could use some kind of nearest neighbor approach to determine which cluster is closest to some novel data sample. Then, you take as a prediction for the new data sample the forecast prediction (i.e. label) that is associated with the closest cluster/prototype etc. But strictly speaking, as soon as you start turning forecast values (which were previously part of some unlabeled time-series dataset) into labels, you turn the training procedure of course into a supervised technique again.