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Yesterday, the Munich datageeks Data Day took place. It was a totally fun event – great to see how much is going on, data-science-wise, in and around Munich, and how many people are interested in the topic! (By the way, I think that more than half the talks were about deep learning!)

I also had a talk, “Time series shootout: ARIMA vs. LSTM” (slides on RPubs, github).

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!

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