Whatever the title, it was really about showing a systematic comparison of forecasting using ARIMA and LSTM, on synthetic as well as real datasets. I find it amazing how little is needed to get a very decent result with LSTM – how little data, how little hyperparameter tuning, how few training epochs.
Of course, it gets most interesting when we look at datasets where ARIMA has problems, as with multiple seasonality. I have such an example in the talk (in fact, it’s the main climax ;-)), but it’s definitely also an interesting direction for further experiments.
Thanks for reading!