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I have data on reservation requests for hotels (your booking information:searching date, check-in, check-out, # of rooms and etc. on hotel booking websites) and am trying to do some analysis on one variable called advance day (i.e. check-in date subtract booking date). My goal is to predict, say using 7 days' of data, the most frequent requests on the next day so that I can prepare room prices in advance.

I tried analyzing hourly modes because that's the stat that describes "most frequent" and my data are not regularly spaced. After creating a time series in R, I tried fitting several models, like ARIMA, HoltWinters smoothing and ETS. Below is the example result of forecasting using HoltWinters smoothing. I am not satisfied with my results because none of the forecasts match with my future (8th day's) modes perfectly. I guess it's because these modes are independent? Everybody search independently? Am I missing anything? Is analyzing modes a good idea?

I have no idea what other ways I could try. Any suggestions besides analysis on modes? Any help would be greatly appreciated. Thanks!

Sophie

the red line is my fitted values.

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