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Is there any function option to take care of autocorrelation of the residuals? #1622
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This seems like a useful analysis, thanks for bringing this up. There isn't any functionality around autocorrelation analysis. As to whether or not I'd worry about seeing autocorrelation in the residuals - if there is any periodic autocorrelation then that would suggest there is a missing seasonal effect. That knowledge (and the period extracted from the autocorrelation function) could be used to add a new seasonality. In this case where it is exponentially decaying, I'd probably expect that in most forecasts. The Prophet trend model is piecewise linear. There are probably more fine-grained trend fluctuations than that, which would be getting picked up by the noise component of the model. From a practical point of view I suppose one could use this knowledge to fine-tune the forecast slightly by using yesterday's residual to bias today's forecast. But whether or not it's worth it would probably depend on how much autocorrelation there is, how quickly it decays, and how much you care about forecasting at that time range vs. longer. |
Hi there!
I am new to the Prophet and already start loving this. I read through the HTML doc and the PeerJ Preprint paper along with some play-around of the API on Python.
As far as I understand, the Prophet does not care about the autocorrelation in the residuals, and when I tested on the Peyton Manning data, the residual had ACF being exponentially decaying and PACF being high rise at lag=1 with close to zero at lag >=2, which implied the residual followed AR(1).
Is there any function option to run on autocorrelated data, or do you expect to have any in the future, or was there any discussion already that we do not have to worry?