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Forecasting stationary data that has no trend/seasonality

  1. May 4, 2015 #1
    1. The problem statement, all variables and given/known data

    We've got a random variable that appears to have no trend/seasonality, is stationary, and we want to forecast it.
    The variable is number of warranty claims received each day, 53 days, so we've got 53 values, and we want to forecast the values of the upcoming 5 days.

    2. The attempt at a solution

    I'm trying to model the data using ARIMA model.

    Judging from the autocorrelation plots, the data is stationary, so no differencing should be done. Judging from the ACF and PACF plots, our best bet would be AR(1), MA(1) or ARIMA(1,0,1). All yield similar results:

    results1.png results2.png forecast.png

    Is there no better way to forecast this variable? ARIMA does not seem like a good forecasting option in this case. The data has no apparent trends/seasonalities and is stationary. What method would be best to forecast such data?
  2. jcsd
  3. May 4, 2015 #2


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    Science Advisor
    Gold Member

    ARIMA is a very powerful and general method for modeling time series. I don't know what you might try that would be better.
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