Answers ( 3 )

  1. swaplaw007
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    when you find the parameters for the ARIMA model from the ACF and PACF plots,
    you get to know how man lags are significant. so you need to test various models
    upto those lags to find out the best model. Suppose, i find that ACF and PACF plots
    are showing significant coefficients upto past 3 lags then i will test various ARIMA models like
    ARIMA(1,0,0) ARIMA(1,0,1) ARIMA(1,0,2) ARIMA(1,0,3)
    ARIMA(2,0,0) ARIMA(2,0,1) ARIMA(2,0,2) ARIMA(2,0,3)
    ARIMA(3,0,0) ARIMA(3,0,1) ARIMA(3,0,2) ARIMA(3,0,3)

    AIC refers to Akaike Information criteria and is a criterion for a selection
    among the finite set of models. The model with the lowest AIC is the best model.

    Best answer
  2. Ognish
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    Akaike information criterion (AIC) is a fined technique based on in-sample fit to estimate the likelihood of a model to predict/estimate the future values.

    A good model is the one that has minimum AIC among all the other models. The AIC can be used to select between the additive and multiplicative Holt-Winters models.

    A set of ARIMA models are used to compare the performance by AIC and BIC values are used as model selection criteria. The optimal model is selected based on the highest R2 and minimum AIC and BIC.

  3. Shubham Bhatt
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    AIC is an estimate of a constant plus the relative distance between the unknown true likelihood function of the data and the fitted likelihood function of the model, so that a lower AIC means a model is considered to be closer to the truth.
    To use AIC for model selection, we simply choose the model giving smallest AIC over the set of models considered.
    Example : Which produces a better model , the ARIMA(2,0,1) model with constant or the ARIMA(2,1,1) model with zero constant.
    AIC = 16.856 (for arima(2,0,1) vs. AIC = 29.267 for arima(2,1,1)
    Thus, we choose the ARIMA(2,0,1) as the better model because the AIC is low for this model.

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