Suppose we have an hotel and we know all the reservations of the last five years. We would like to forecast/estimate the room demand day-by-day for the next year.
I'm a mathematician but not a statistician, I'm sorry if I'm saying something trivial or dumb.
I'm wondering if a "standard model" (as ARMA, ARIMA, SARIMA) exists for such a problem. The main doubt I have, it is about the regularity of the variable "# of reservations". Is it predictable?
As a mathematician I can understand a model for forecasting, but I don't really know the domain of application of such a model.
Let's take the example of Christmas day. It is a "regular event" because it is the same day every year, so I imagine I can easily make a prediction, but how about Easter? Obviously I cannot estimate it by the data I have, because the actual day of Easter changes every springtime, I need an extraordinary information.
- Is it a time-series analysis problem?
- standard models work for this problem? (I have just read about ARMA, ARIMA, SARIMA and linear models, looking for time series)
- If not, do other models exist for treating this case?
- Or basically we need to invoke the experience of the people who work in the hotel? :)
Looking on the internet I found many things on "forecasting tourism demand", but all these analysis try to estimate the number of tourists per month (by the query on google, by the last years data, etc.). But maybe I'm wrong.
I'm trying to get an idea out of the comments posted so far. I know that, even Christmas, it's more complicated because it depends on the day ("Christmas on a Thursday will lead to Friday being taken to create a four-day weekend, for example" cit. @Wayne). But the point is the same: are these complications "treatable" or not?
@AnscombesGimlet said they are treatable, but I'm wondering: how many manual corrections do I have to do in order to have a suitable model? If I have to do a lot of corrections, of course I'm still using a "model", but I won't say it's "standard" and I would answer "No" to 2. Up to what I've understood (and I think it's the same point of @IrishStat), each "hotel" has its own history and its own variables/coefficients: a mechanism for understanding these peculiarities does not exist(well, let's call it experience or savoir-faire).
Tell me if I'm wrong. Suppose I'm one of the best statisticians trained in the black art of time series, what would I do to tackle this problem? Here my set list:
- Prepare all the datas from my hotel
- Start plotting datas with different time scales years/(seasons)/months/(weeks)/days
- Finding maybe different "time scales" (instead of plotting january-february etc, maybe 15th of january to 15th of february etc because the data looks more uniform)
- Start using my favorite method (let's say ARIMA) and start doing some projection about my data, with the parameters I think fit better the data I have already studied.
- Make the model a good model.
I won't do: 1. Prepare all the datas from my hotel 2. Take e.g. ARIMA with "standard parameters" that I imagine would work because more or less I think I will have a pick for the summer and for holidays 3. Launch it and then try to understand the parameters of ARIMA looking how far is the model from the reality 4. Approximate the model to a good one.
The difference between these two approaches can be perceived in this example:
What if I change the hotel? The first technique (which I would call "non standard") has to be applied anew if I take a different hotel (and I will spend a lot of time on it). The second one(which I would call "standard") could be implemented (more or less) for every hotel.
So now the question is: can both methods work? are both reliable?