Dynamic forecasts – with Bayesian linear models and neural networks (talk at Predictive Analytics World Berlin)

I really wish I had the time to write an article about the conference, instead of just posting the slides!

Predictive Analytics World was super inspiring, not just in a technical way but also as to the broader picture of today’s data science / AI explosion, including its political, sociological and personal implications.

As I really don’t have the time, I’m not even gonna try, so let me just point you to my talk, which was about time series forecasting using two under-employed (as yet) methods: Dynamic Linear Models (think: Kalman filter) and Recurrent Neural Networks (LSTMs, to be precise).

So, here are the slides, and as usual, here’s the link to the github repo, containing some more example code.

For me, experimentation with time series forecasting seems to form a time series in itself – I’m sure there’s pretty much still to be explored 🙂
Thanks for reading!

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Time series shootout: ARIMA vs. LSTM (talk)

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!

Time series prediction – with deep learning

More and more often, and in more and more different areas, deep learning is making its appearance in the world around us.
Many small and medium businesses, however, will probably still think – Deep Learning, that’s for Google, Facebook & co., for the guys with big data and even bigger computing power (barely resisting the temptation to write “yuge power” here).

Partly this may be true. Certainly when it comes to running through immense permutations of hyperparameter settings. The question however is if we can’t obtain good results in more usual dimensions, too – in areas where traditional methods of data science / machine learning prevail. Prevail, as of today, that is.

One such area is time series prediction, with ARIMA & co. top on the leader board. Can deep learning be a serious competitor here? In what cases? Why? Exploring this is like starting out on an unknown road, fascinated by the magical things that may await us 😉
In any case, I’ve started walking down the road (not running!), in a rather take-your-time-and-explore-the-surroundings way. That means there’s much still to come, and it’s really just a beginning.

Here, anyway, is the travel report – the presentation slides, I mean: best viewed on RPubs, as RMarkdown on github, or downloadable as pdf).
Enjoy!