There are at least a few discussions and examples/MWEs around this kind of univariate time series forecasting with Flux RNNs floating around community forums. How to train Flux to learn a sequence conditional to some initial "seeds"? is a recent example I just remembered. The reason such a thing does not exist in the model zoo is probably two-fold:
- Model zoo entries don’t write themselves
- A LSTM is a big hammer to model a 30-sample univariate timeseries forecasting problem with. Generally we try to strike a balance between clear, brief files and sufficiently “common” or “interesting” datasets and tasks in the model zoo to differentiate it from tutorials. In this case, perhaps something like forecasting with a UCI benchmark dataset would be appropriate.