2
$\begingroup$

I am analyzing a time series (hospital admittance) registered by date - time at hourly frequency, collected all days during 2010 - 2014 (5 years). The time series exhibits seasonality at multiple level (hour, weekday, month). The conditional distribution could be reasonably modeled using a count distribution (Poisson, NegBin). I wonder what could be the best approach for modeling this. I was thinking about a Poisson loglinear model, say $$E\left[admittance_t\right]= \alpha * hour * weekday * month,$$ but I do not know how to account for autocorrelation. I s there an approach to perform this in R? Are there possible alternatives in R?

$\endgroup$
  • 2
    $\begingroup$ I don't know that I would expect autocorrelation to be a big problem here. Will your model be regularized? $\endgroup$ – jlimahaverford Oct 5 '15 at 21:10
1
$\begingroup$

Collect your data into hourly time buckets and build 24 hourly models and a daily model . Allow top-down and bottom up reconcilliation. Make sure that your daily model includes daily/weekly/monthly/holiday/long-weekend effects etc along with appropriate level shifts/time trends and possible changes in daily patterns along with specific days-of-the-month effects. For example a daily model might encompass enter image description here and possibly specific weeks within a month or weeks within a quarter and effects like a Monday after a Friday holiday or a Friday before a Monday holiday. If you post your data with some info regarding country source and start date,I will try to be more explicit.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.