Sliding window approach using SVR & LightGBM
Tensorflow is designed to really make your life easier (especially with the fancy new additions with the 2.0 version). I think you should use pandas to generate sliding windows: I would think that for a N sized time series with K lookback window you will have N-K+1 examples by sliding lookback.

And while you're at it have you treated your lookback window as a hyperparameter? i.e optimise your model performance on a validation test by searching the best lookback?