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Determining Optimum Order of Autoregressive Model and the Application to Long-range Forecast
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摘要: 对自回归模型的5种定阶方法（FPE、AIC、BIC、L1和L2准则）作了概述，并应用上述方法对青岛月平均温度序列进行了自回归模型定阶试验。结果指出，FPE、AIC和L1准则选择自回归模型的阶数较高，L2准则选择自回归的阶数为中等，BIC准则确定的阶数最低。文章还提出了一个应用自回归模型递推预报月平均温度的方法，预报实践证明，由BIC准则产生的低阶自回归模型的效果优于其它方法。Abstract: The methods of determining the optimum order of autoregressive (AR) models, such as FPE, AIC, BIC, L1and L2 were summarized and tested by using the monthly mean temperature data in Qingdao. The selected orders of the AR model by use of the FPE, AIC and L1 criteria are the highest, medium by L2 and the lowest by BIC, respectively. Additionally, a recurrence method of AR model was suggested to forecast the monthly mean temperatures in Qingdao. It has been proved by the forecast practice that the low order AR model from the BIC criterion is more efficient
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