[Q] Would Gaussian kernel smoothing work well for arrival pattern?
I was pretty much wondering if using Gaussian kernel smoothing would be a good method or if there are other statistical methods one can be leveraging.
Some context:
I’m working on estimating arrival patterns to an airport check-in queue. I have noisy sensor data with queue exits per minute, estimated waiting time, and queue length. I estimate arrivals roughly as:
arrival_time ≈ exit_time - waiting_time
Then I use a window like T-250 to T-40 minutes before departure, aggregate into non-overlapping 5-min buckets using sums, normalize each curve, and average over ~30 days.
For smoothing the final empirical 1D curve, would Gaussian kernel smoothing make sense, or would a simple moving average / Weibull / Gamma fit be more appropriate?
I’m not assuming arrivals are normally distributed, the Gaussian kernel would only be used for smoothing.
Appreciate all the suggestions I can get 😊