我在列表中的多个ts变量上执行了auto.arima函数
arima_train <- lapply(train_data, function(x) auto.arima(x$Value))
我可以获得所有变量的函数摘要
> for (i in (1:16)) summary(arima_train[[i]])
Series: x$Value
ARIMA(0,1,0)
sigma^2 estimated as 2.808: log likelihood=-137.4
AIC=276.81 AICc=276.86 BIC=279.07
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.0451375 1.664175 1.228471 0.04069765 2.268046 0.9866678 -0.188887
Series: x$Value
ARIMA(0,0,0) with non-zero mean
Coefficients:
intercept
5251.6806
s.e. 187.3747
sigma^2 estimated as 2563468: log likelihood=-632.91
AIC=1269.81 AICc=1269.99 BIC=1274.37
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -2.829471e-12 1589.926 1012.06 -6.179073 17.3668 0.8272841 0.06356198
Series: x$Value
ARIMA(1,1,0) with drift
Coefficients:
ar1 drift
0.4006 0.3324
s.e. 0.1086 0.0907
sigma^2 estimated as 0.2205: log likelihood=-46.04
AIC=98.08 AICc=98.44 BIC=104.87
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.0003775476 0.4597061 0.3308142 0.0007945521 0.1444236 0.669588 0.05640966
Series: x$Value
ARIMA(0,1,0) with drift
Coefficients:
drift
54.8873
s.e. 14.8586
sigma^2 estimated as 15900: log likelihood=-443.67
AIC=891.34 AICc=891.51 BIC=895.86
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.07422375 124.3296 99.95529 -0.08126397 1.520543 0.9287823 -0.03885156
Series: x$Value
ARIMA(0,2,1)
Coefficients:
ma1
-0.9171
s.e. 0.0565
sigma^2 estimated as 100261: log likelihood=-502.59
AIC=1009.17 AICc=1009.35 BIC=1013.67
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 68.51967 309.9734 221.9339 0.04873783 0.1559967 0.9006235 -0.1312991
Series: x$Value
ARIMA(1,2,1)
Coefficients:
ar1 ma1
0.2549 -0.9151
s.e. 0.1297 0.0538
sigma^2 estimated as 0.8075: log likelihood=-91.49
AIC=188.98 AICc=189.34 BIC=195.72
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.1095264 0.8733085 0.6821252 -0.08248981 0.5255063 0.8663844 -0.02072697
Series: x$Value
ARIMA(2,1,2)
Coefficients:
ar1 ar2 ma1 ma2
-0.1269 0.4314 0.7658 0.3584
s.e. 0.1816 0.1818 0.1826 0.1581
sigma^2 estimated as 0.04297: log likelihood=12.48
AIC=-14.95 AICc=-14.03 BIC=-3.64
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.006785029 0.199965 0.1498849 1.903016 13.18623 0.6692976 -0.01844039
Series: x$Value
ARIMA(0,2,2)
Coefficients:
ma1 ma2
-0.2629 -0.5857
s.e. 0.0920 0.0893
sigma^2 estimated as 1.257: log likelihood=-106.99
AIC=219.97 AICc=220.33 BIC=226.72
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.1654204 1.089599 0.8857888 -0.1206975 0.6571573 0.8396662 0.08537194
Series: x$Value
ARIMA(0,0,0) with non-zero mean
Coefficients:
intercept
0.25
sigma^2 estimated as 0: log likelihood=Inf
AIC=-Inf AICc=-Inf BIC=-Inf
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0 0 0 0 0 NaN NaN
Series: x$Value
ARIMA(0,1,1)
Coefficients:
ma1
-0.3715
s.e. 0.1246
sigma^2 estimated as 877.4: log likelihood=-340.9
AIC=685.8 AICc=685.97 BIC=690.32
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1.621179 29.20693 21.85996 -0.05931622 5.767894 0.9993928 0.03373764
Series: x$Value
ARIMA(1,2,1)
Coefficients:
ar1 ma1
0.2877 -0.9395
s.e. 0.1332 0.0528
sigma^2 estimated as 0.07365: log likelihood=-7.22
AIC=20.43 AICc=20.82 BIC=27
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.02910269 0.2632887 0.193597 -0.02640935 0.181013 0.754128 -0.06465092
Series: x$Value
ARIMA(0,0,0) with non-zero mean
Coefficients:
intercept
0.3792
s.e. 0.0827
sigma^2 estimated as 0.4989: log likelihood=-76.62
AIC=157.25 AICc=157.42 BIC=161.8
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -3.251722e-14 0.7013751 0.512037 -Inf Inf 0.7256413 -0.09038341
Series: x$Value
ARIMA(0,1,0) with drift
Coefficients:
drift
-88.7606
s.e. 28.2956
sigma^2 estimated as 57661: log likelihood=-489.4
AIC=982.8 AICc=982.98 BIC=987.33
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.2040244 236.7675 186.6802 -0.07011135 1.570809 0.9429632 -0.08296941
Series: x$Value
ARIMA(0,1,1) with drift
Coefficients:
ma1 drift
-0.8659 0.0907
s.e. 0.1172 0.0137
sigma^2 estimated as 0.4673: log likelihood=-73.42
AIC=152.83 AICc=153.19 BIC=159.62
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.04660173 0.6692201 0.4396176 -0.6847646 3.393219 0.9150644 0.1119422
Series: x$Value
ARIMA(2,1,2) with drift
Coefficients:
ar1 ar2 ma1 ma2 drift
1.3171 -0.8973 -1.2696 0.7378 0.1369
s.e. 0.0973 0.0889 0.1584 0.1495 0.0916
sigma^2 estimated as 0.9726: log likelihood=-97.5
AIC=207.01 AICc=208.32 BIC=220.58
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.007610831 0.9441978 0.627121 23.63108 55.34784 0.9207112 -0.00091189
Series: x$Value
ARIMA(1,0,0) with non-zero mean
Coefficients:
ar1 intercept
0.3912 0.1423
s.e. 0.1076 0.0368
sigma^2 estimated as 0.03781: log likelihood=16.67
AIC=-27.34 AICc=-26.99 BIC=-20.51
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.000930651 0.1917348 0.1362479 -Inf Inf 0.8794182 0.05445404
我想创建多个向量，这些向量应该能够为所有16个变量保存以下信息
谢谢。
答案 0 :(得分：1)
您可以使用arima_train$coef和arima_train$aic获取AIC和对数似然。对于系数，请使用coef(arima_train)。您可以通过对系数求和来获得订单