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Thread: Forecasting times eries with different daily length

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    Forecasting times eries with different daily length




    Hey everybody!

    Iíve been tasked by my company to forecast the amount of phone calls on an hourly basis during the companyís opening hours.

    That all went pretty well - until I realized, that my companyís opening hours for phone calls is 9am to 3pm (6 hours total) on Mondays and 9am to 12am (3 hours) the remaining weekdays. That implies that Iíve some sort of ďunbalancedĒ timeseries, because of the three extra hours every Monday.

    As I see it that completely destroys my chance of utilizing an ARIMA-model, since the AR and MA terms canít handle the unbalances.

    Iíve been searching for some time now, and I really need some sort of strategy for this problem. My currently ďfallbackĒ strategy is to use a normal regression with dummies for every hour. This is however maybe not the best way forward.

    So what Iím asking is; maybe you guys have experience with a problem like this, that you would like to share, or maybe some literature on the problem.

    Iím currently working in SAS with the timeseries module (SAS ETS), so if you know of any procedure, that would also be much appreciated.

    Andreas

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    Re: Forecasting times eries with different daily length

    hi,
    a dumb question: why not model Mondays separately from the rest of the days? Or, better, have a Monday model, a Friday model and a mid-week model, as I kindof assume Fridays will also he different from the other days even if the opening hours are the same.

    regards

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    Re: Forecasting times eries with different daily length

    Hey Rogojel.

    First off - sorry for the mistype in the title. And thanks for the input. Really appreciate it.

    The short answer; I didnít really think of that. And I could be a potential solution. The actual situation is that Iím trying to estimate the number of phone calls (on an hourly basis) as a function off letters the company has sent to the customer (amount on a daily basis) in the period up to the given date. The general level of phone calls might be more correlated with the previous days than the previous day of the week.
    Iím pretty emptyhanded right now, but I would at least like to give it a try. If the model doesnít fit the data, then so be it.

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    Re: Forecasting times eries with different daily length

    hi,
    maybe you could first model the total number of calls per day? That would probably be easier. Then, as a second step, model the conditional distribution for a day given that the total number for the day is X and there are Y hours available for a call.

    regards

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    Re: Forecasting times eries with different daily length


    hi!

    Thanks, to me that sounds like a good strategy! I'll think I'll give it a go. Thanks for the input - I really appreciate it.


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