在使用auto.arima时,我们得到了最佳的pd和q值。有没有一种方法可以动态地将这些值发送到arima()函数中? 库(预测) 你应该这样做:
下面是一个小例子在使用auto.arima时,我们得到了最佳的pd和q值。有没有一种方法可以动态地将这些值发送到arima()函数中? 库(预测) 你应该这样做:,r,arima,R,Arima,下面是一个小例子 library(forecast) fit_m <- auto.arima(dataset1, p=0, d=1, max.d =2, max.p=2, max.q=2, max.P=2, max.Q=2, max.D=2, start.p =0, start.q=0, start.P=0, start.Q=0, stepwise=TRUE, trace=TRUE) fit_m_fina
library(forecast)
fit_m <- auto.arima(dataset1, p=0, d=1, max.d =2, max.p=2, max.q=2,
max.P=2, max.Q=2, max.D=2, start.p =0, start.q=0,
start.P=0, start.Q=0, stepwise=TRUE, trace=TRUE)
fit_m_final <- arima(dataset1, c(0, 1, 0), seasonal=list(order=c(0, 1, 0),
period=12))
我建议您查看Rob J Hyndman教授和George Athanasopoulos教授(预测包的作者)所写的免费报告。您应该这样做:
下面是一个小例子
library(forecast)
fit_m <- auto.arima(dataset1, p=0, d=1, max.d =2, max.p=2, max.q=2,
max.P=2, max.Q=2, max.D=2, start.p =0, start.q=0,
start.P=0, start.Q=0, stepwise=TRUE, trace=TRUE)
fit_m_final <- arima(dataset1, c(0, 1, 0), seasonal=list(order=c(0, 1, 0),
period=12))
我建议您查看Rob J Hyndman教授和George Athanasopoulos教授(预测包的作者)所写的免费报告。这似乎有效
x$arma
[1] 2 1 0 1 12 1 1
例1
例2
只需根据您的需要调整
auto.arima
部分中的特殊参数。这似乎有效
x$arma
[1] 2 1 0 1 12 1 1
例1
例2
只需根据您的需要调整auto.arima
部分中的特殊参数即可
auto.arima(wineind)
# Series: wineind
# ARIMA(1,1,2)(0,1,1)[12]
#
# Coefficients:
# ar1 ma1 ma2 sma1
# 0.4299 -1.4673 0.5339 -0.6600
# s.e. 0.2984 0.2658 0.2340 0.0799
#
# sigma^2 estimated as 5399312: log likelihood=-1497.05
# AIC=3004.1 AICc=3004.48 BIC=3019.57
arima(wineind,
order=auto.arima(wineind)$arma[c(1, 6, 2)],
seasonal=list(order=auto.arima(wineind)$arma[c(3, 7, 4)],
period=auto.arima(wineind)$arma[5]))
# Call:
# arima(x = wineind, order = auto.arima(wineind)$arma[c(1, 6, 2)],
# seasonal = list(order = auto.arima(wineind)$arma[c(3, 7, 4)],
# period = auto.arima(wineind)$arma[5]))
#
# Coefficients:
# ar1 ma1 ma2 sma1
# 0.4299 -1.4673 0.5339 -0.6600
# s.e. 0.2984 0.2658 0.2340 0.0799
#
# sigma^2 estimated as 5266773: log likelihood = -1497.05, aic = 3004.1
auto.arima(woolyrnq)
# Series: woolyrnq
# ARIMA(1,0,0)(0,1,1)[4]
#
# Coefficients:
# ar1 sma1
# 0.8077 -0.6669
# s.e. 0.0629 0.0944
#
# sigma^2 estimated as 175880: log likelihood=-858
# AIC=1722 AICc=1722.21 BIC=1730.23
arima(woolyrnq,
order=auto.arima(woolyrnq)$arma[c(1, 6, 2)],
seasonal=list(order=auto.arima(woolyrnq)$arma[c(3, 7, 4)],
period=auto.arima(woolyrnq)$arma[5]))
# Call:
# arima(x = woolyrnq, order = auto.arima(woolyrnq)$arma[c(1, 6, 2)],
# seasonal = list(order = auto.arima(woolyrnq)$arma[c(3, 7, 4)],
# period = auto.arima(woolyrnq)$arma[5]))
#
# Coefficients:
# ar1 sma1
# 0.8077 -0.6669
# s.e. 0.0629 0.0944
#
# sigma^2 estimated as 172819: log likelihood = -858, aic = 1722