Poisson processes for many related univariate time series

Hi everyone, I’ve been wanting to do some time series forecasting with Turing, because I’d like forecasting intervals as well.

For practice, I’d like to forecast the norway car sales dataset. In short, it has ten years of monthly car sales data, from 2007 to 2017.

  • Indices (rows) are a monthly observations of car sales.

  • Columns are car brands.

I would like to model this as time series of counts, as well as including the fact that some sub-groups are probably linked together (depending on the economic conjoncture).

I’m not sure at all how I would go about doing this in Turing though, even something simple with one lag for example.

Can you guys guide me ?


Do you have a model in mind? It’s a little tough to help without some kind of model framework.


For now, something simple like an GLM with a time component as in this pymc3 example. Ideally I’d like to try GLARMA with a poisson distribution as in this R package, but that’s a bit too much for now.

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Tight, we’ve actually got a tutorial for GLM here. If you want to see a different link distribution (logistic) you can check out our logistic regression tutorial here.

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Thanks i appreciate it