借助ARIMA预白化辅助解读时间序列CCF时arima函数报错如何解决

R标准stats包的小写arima()函数没有model参数，无法直接传入已拟合的模型对象复用阶数配置，因此触发了「unused argument」报错。

你可以选择以下任意一种方案修复代码：

方法1：手动复用ARIMA阶数参数（无需额外安装包） #

直接把GDP序列使用的ARIMA阶数同步给能源消耗序列的拟合逻辑即可，修正后完整代码如下：

energy <- data.frame(consumption = c(3319,2986,3234,3362,4146,5355,8421,9163,4867,3668,3378,3409,3250,2867,3067,2965,3490,5557,8985,8693,5548,3786,3373,3369,3241,3107,3212,3067,3398,6010,9236,9059,5067,4104,3344,3460,3176,2910,3190,3241,4401,5489,7501,8714,5733,3810,3309,3387,3216,2282,3157,3398,3904,5537,8326,8384,4463,3797,3322,3417,3224,2797,3201,3055,4695,4939,7926,8690,5297,3826,3221,3311,3229,2802,3082,3594,3870,5493,8294,8718,4611,3799,3211,3303,3322,2828,2899,3210,4044,5393,7430,8748,5099,3116,3133,3196,3078,2768,2863,3418,4033,4938,6379,7845,5106,3679,3310,3337))
gdp <- data.frame(value = c(17751,17751,17751,18138,18138,18138,18092,18092,18092,18110,18110,18110,15833,15833,15833,16547,16547,16547,16696,16696,16696,15698,15698,15698,14500,14500,14500,15180,15180,15180,15451,15451,15451,15056,15056,15056,13726,13726,13726,14681,14681,14681,15229,15229,15229,14901,14901,14901,13846,13846,13846,14770,14770,14770,15413,15413,15413,14915,14915,14915,13823,13823,13823,14776,14776,14776,15119,15119,15119,14984,14984,14984,13583,13583,13583,14606,14606,14606,15362,15362,15362,14864,14864,14864,13633,13633,13633,14791,14791,14791,15846,15846,15846,14892,14892,14892,13850,13850,13850,15033,15033,15033,16091,16091,16091,15111,15111,15111,13896,13896,13896,15336,15336,15336,16680,16680,16680,15289,15289,15289,13694,13694,13694,12947,12947,12947,15119,15119,15119,14392,14392,14392))

# 拟合GDP的ARIMA模型
gdp.model <- arima(gdp$value, order = c(0,1,0), seasonal = list(order = c(1,1,0), period = 12))
pwx <- residuals(gdp.model)
# 用相同阶数拟合能源序列的ARIMA模型
pwy <- residuals(arima(energy$consumption, order = c(0,1,0), seasonal = list(order = c(1,1,0), period = 12)))
ccf(pwx,pwy,20)

如果不想重复写参数，也可以直接从已拟合的gdp.model中提取阶数配置，通用写法如下：

pwy <- residuals(arima(energy$consumption, order = gdp.model$arma[c(1,6,2)], seasonal = list(order = gdp.model$arma[c(3,7,4)], period = gdp.model$arma[5])))

方法2：使用forecast包的Arima函数（支持model参数） #

如果希望直接复用已拟合的模型结构，不需要手动提取参数，可以安装并加载forecast包，它提供的大写Arima()函数支持model参数传入已拟合对象：

# 首次运行先执行安装：install.packages("forecast")
library(forecast)

energy <- data.frame(consumption = c(3319,2986,3234,3362,4146,5355,8421,9163,4867,3668,3378,3409,3250,2867,3067,2965,3490,5557,8985,8693,5548,3786,3373,3369,3241,3107,3212,3067,3398,6010,9236,9059,5067,4104,3344,3460,3176,2910,3190,3241,4401,5489,7501,8714,5733,3810,3309,3387,3216,2282,3157,3398,3904,5537,8326,8384,4463,3797,3322,3417,3224,2797,3201,3055,4695,4939,7926,8690,5297,3826,3221,3311,3229,2802,3082,3594,3870,5493,8294,8718,4611,3799,3211,3303,3322,2828,2899,3210,4044,5393,7430,8748,5099,3116,3133,3196,3078,2768,2863,3418,4033,4938,6379,7845,5106,3679,3310,3337))
gdp <- data.frame(value = c(17751,17751,17751,18138,18138,18138,18092,18092,18092,18110,18110,18110,15833,15833,15833,16547,16547,16547,16696,16696,16696,15698,15698,15698,14500,14500,14500,15180,15180,15180,15451,15451,15451,15056,15056,15056,13726,13726,13726,14681,14681,14681,15229,15229,15229,14901,14901,14901,13846,13846,13846,14770,14770,14770,15413,15413,15413,14915,14915,14915,13823,13823,13823,14776,14776,14776,15119,15119,15119,14984,14984,14984,13583,13583,13583,14606,14606,14606,15362,15362,15362,14864,14864,14864,13633,13633,13633,14791,14791,14791,15846,15846,15846,14892,14892,14892,13850,13850,13850,15033,15033,15033,16091,16091,16091,15111,15111,15111,13896,13896,13896,15336,15336,15336,16680,16680,16680,15289,15289,15289,13694,13694,13694,12947,12947,12947,15119,15119,15119,14392,14392,14392))

# 拟合GDP的ARIMA模型
gdp.model <- Arima(gdp$value, order = c(0,1,0), seasonal = list(order = c(1,1,0), period = 12))
pwx <- residuals(gdp.model)
# 直接传入model参数复用模型结构
pwy <- residuals(Arima(energy$consumption, model = gdp.model))
ccf(pwx,pwy,20)

内容的提问来源于stack exchange，提问作者rdev