TLDR: The most important thing about communicating uncertainty is that you’re doing it.

Want all the formulae? presentation, github

🙂

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# recurrent null

## Data Science, Machine Learning, & diverse IT stuff

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ARIMA

# Plus/minus what? Let’s talk about uncertainty (talk)

# Time series shootout: ARIMA vs. LSTM (talk)

# Time series prediction – with deep learning

Last week at DOAG 2017, I had two talks, one about deep learning with DL4J (slides here) and one about how to communicate uncertainty (or rather: how to construct prediction intervals for various methods / in various frameworks ranging from simple linear regression over Bayesian statistics to neural networks).

TLDR: The most important thing about communicating uncertainty is that you’re doing it.

Want all the formulae? presentation, github

🙂

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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!

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!