We have a time series dataset: Daily arrivals of asylum seekers. Goal is to model this variable. In particular we would like to attempt Arima modeling and/or fitting a distribution.
Before we get to questions of stationarity, constant variance and such, we are at a loss about correlatedness of observations: In case of a holiday, the next day will see more people coming in. What are standard methods for taking such interdependence between observations into account?
We may group per week or per month. Per week still gives this problem however. Per month leaves us with rather little observations and loss of information.
Currently we make prediction by one-sided moving average. But the time window is chosen arbitrary, we want to obtain more statistical foundation for our predictions.