Hi
I am forecasting the number of logins. I have a dataset with the number of logins for each hour.
First, I use LOF (local outlier factor) to find the outliers and then I remove them.
Second, I use Kalman filter to forecast.
But as you can see in the plot, the prediction (blue) is displaced in time from the logins (red) and also from the future confidence interval (green).
So, would it be better to transform the outliers (maybe make them "normal") rather than completely removing them? Because right now I have some gaps in time. For example (the first monday), I have logins , for each hour, from 00h to 15h and the from 18h until the next day. So there is a gap of 2 hours.
Thank you
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