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Thread: Regression Type Estimation

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    Question Regression Type Estimation




    Hello,

    Attached is a time series data that has a plot of the percentage of breakdowns on a scheduled hours across the days. The objective is to predict the value for the next day.

    I tried to use linear regression to fit the data, but got a p-value higher than 0.05 and R square value less than 0.1. I also tried ploynomial regression but the got higher p-values.

    Tried ARIMA but the RMSE was high.

    Can someone advise me with the different options to fit the data ?
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    Last edited by Mukund; 01-20-2016 at 07:35 AM.

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    Re: Regression Type Estimation

    You should never do linear regression with time series as it violates the assumption of linear regression normally.

    I would try exponential smoothing, a family of time series that includes Holt Winston, Seasonal, Damped trend, Single smoothing and the like. Its arguably the easiest of the true statistical time series models to use. Personally I use MAPE rather than RMSE to assess predictions, but everyone has their own preference
    "Very few theories have been abandoned because they were found to be invalid on the basis of empirical evidence...." Spanos, 1995

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    Mukund (01-21-2016)

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    Re: Regression Type Estimation

    Hello,

    Thank you for your tip. I will try these methods and let you know how it went.

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    Re: Regression Type Estimation

    Quote Originally Posted by noetsi View Post
    You should never do linear regression with time series as it violates the assumption of linear regression normally.

    I would try exponential smoothing, a family of time series that includes Holt Winston, Seasonal, Damped trend, Single smoothing and the like. Its arguably the easiest of the true statistical time series models to use. Personally I use MAPE rather than RMSE to assess predictions, but everyone has their own preference
    Thank you for the tip. I will try these methods.

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    Re: Regression Type Estimation


    It probably could break the normality of residuals assumption, but what it does in particular is break the independence of errors assumption, since errors from the same unit (person, thing being observed) are correlated.
    Stop cowardice, ban guns!

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