Many thanks for releasing this! the effort to do so in both R and Python, with all the supporting documentation, is very appreciated.
Perhaps a more general or 'motivational' question - what are some common use cases for accurate events/clicks predictions? If it is just a trend that someone is interested in then an analyst can 'eyeball' it pretty well from the time series plot. I guess the need for high accuracy is driven by more than just spotting a trend?
We don't really use Prophet for event prediction. It's mostly for predicting aggregates that are measured on a slower timescale (daily or weekly data). Accuracy is harder to measure for time series forecast because out of sample is tough to evaluate.
Good question. We tried to make it conform to a more standard fit/predict model. The main difference is that predict takes a dataframe of future dates which we provide a helper function for creating. In most of Rob Hyndman's predict functions, they take a number of periods ahead to forecast instead of an explicit set of dates.
Hi @seanjtaylor. Can you add support for multiplicative models?
I have time series that shouldn't go below zero and I couldn't find a way with your tool to bound it (similar to the way you do growth). I eventually did my own BoxCox transformation prior to modeling and had to inverse the transformation before calling the plot function. It would be great to have this built in.