KNIME model suggestion I have the following model:

a table with events, each row is event, each event has a timestamp.

I want to try and learn how many event happen per day of week per hour.

I tried to work with the naive bayes learner (with binning), but got bad 
results, I think because of the binning, the numbers does have meaning. 

I'm assuming there is a relation between day and hour to the amount of events, some simple formula/tree.

What learner should i use that can work with numbers and not nominal values?

thanks,
You can check this whitepapers for time series prediction with KNIME.
https://www.knime.org/files/knime_bigdata_energy_timeseries_whitepaper.pdf
https://www.knime.org/files/Anomaly_Detection_Time_Series_final.pdf

I am not sure though if you are trying to predict how many events will happen say in the next hour or just count how many events have happened per hour in the past. In this last case, just use a GroupBy node.

Let me know if this helps

-- Rosaria