我已经使用auto.arima函数对91个模型进行了ARIMA建模。输出位于列表列表中。
一种模型的输出结构如下：
List of 19
$ coef : Named num [1:8] -3.17e-01 -3.78e-01 -8.02e-01 -5.39e+04 -1.33e+05 ...
..- attr(*, "names")= chr [1:8] "ar1" "ar2" "ma1" "Price.Diff" ...
$ sigma2 : num 6.37e+10
$ var.coef : num [1:8, 1:8] 1.84e-02 8.90e-03 -7.69e-03 -8.80e+02 2.83e+03 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:8] "ar1" "ar2" "ma1" "Price.Diff" ...
.. ..$ : chr [1:8] "ar1" "ar2" "ma1" "Price.Diff" ...
$ mask : logi [1:8] TRUE TRUE TRUE TRUE TRUE TRUE ...
$ loglik : num -1189
$ aic : num 2395
$ arma : int [1:7] 2 1 0 0 1 1 0
$ residuals: Time-Series [1:87] from 1 to 87: 1810 -59503 263294 240970 94842 ...
$ call : language auto.arima(y = x[, 2], stepwise = FALSE, approximation = FALSE, xreg = x[, 3:ncol(x)], x = list(x = c(1856264.57,| __truncated__ ...
$ series : chr "x[, 2]"
$ code : int 0
$ n.cond : int 0
$ nobs : int 86
$ model :List of 10
..$ phi : num [1:2] -0.317 -0.378
..$ theta: num -0.802
..$ Delta: num 1
..$ Z : num [1:3] 1 0 1
..$ a : num [1:3] -599787 284456 1887763
..$ P : num [1:3, 1:3] 0.00 0.00 -4.47e-23 0.00 3.33e-16 ...
..$ T : num [1:3, 1:3] -0.317 -0.378 1 1 0 ...
..$ V : num [1:3, 1:3] 1 -0.802 0 -0.802 0.643 ...
..$ h : num 0
..$ Pn : num [1:3, 1:3] 1.00 -8.02e-01 -1.83e-23 -8.02e-01 6.43e-01 ...
$ bic : num 2417
$ aicc : num 2398
$ xreg : Time-Series [1:87, 1:5] from 1 to 87: -0.866 -0.466 -1.383 -0.999 -0.383 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:5] "Price.Diff" "Easter" "Christmas" "High.Week" ...
$ x : Time-Series [1:87] from 1 to 87: 1856265 1393925 2200962 2209996 2161707 ...
$ fitted : Time-Series [1:87] from 1 to 87: 1854455 1453429 1937668 1969026 2066864 ...
- attr(*, "class")= chr [1:3] "ARIMA" "forecast_ARIMA" "Arima"
打印后，输出如下：
Series: x[, 2]
Regression with ARIMA(2,1,1) errors
Coefficients:
ar1 ar2 ma1 Price.Diff Easter Christmas High.Week Low.Week
-0.3170 -0.3777 -0.8017 -53931.11 -133187.55 -53541.62 -347146.59 216202.71
s.e. 0.1356 0.1319 0.1069 28195.33 68789.25 23396.62 -74115.78 66881.15
sigma^2 estimated as 6.374e+10: log likelihood=-1188.69
AIC=2395.38 AICc=2397.75 BIC=2417.47
我编写了以下内容以将模型导出为文本文件格式：
# export model outputs to newly created folder
for(i in 1:length(ts_outputs)){
sink(paste0(names(ts_outputs[i]), ".txt"))
print(ts_outputs[i])
sink()
}
此方法可以查看模型输出本身，但是我需要能够将模型输出重新导入R中，以使用它们预测我的时间序列向前。
我假设一旦重新导入，就需要将它们放回原始结构中。
是否已经编写了某个软件包来执行此操作？
文本文件是否适合原始导出？
我相信以下是来自预测包的源代码，该源代码写入了输出（https://rdrr.io/github/ttnsdcn/forecast-package/src/R/arima.R）：
if (length(x$coef) > 0) {
cat("\nCoefficients:\n")
coef <- round(x$coef, digits=digits)
if (se && nrow(x$var.coef)) {
ses <- rep(0, length(coef))
ses[x$mask] <- round(sqrt(diag(x$var.coef)), digits=digits)
coef <- matrix(coef, 1, dimnames=list(NULL, names(coef)))
coef <- rbind(coef, s.e.=ses)
}
print.default(coef, print.gap=2)
}
cm <- x$call$method
if (is.null(cm) || cm != "CSS")
{
cat("\nsigma^2 estimated as ", format(x$sigma2, digits=digits),
": log likelihood=", format(round(x$loglik, 2)),"\n",sep="")
npar <- length(x$coef) + 1
nstar <- length(x$residuals) - x$arma[6] - x$arma[7]*x$arma[5]
bic <- x$aic + npar*(log(nstar) - 2)
aicc <- x$aic + 2*npar*(nstar/(nstar-npar-1) - 1)
cat("AIC=", format(round(x$aic, 2)), sep="")
cat(" AICc=", format(round(aicc, 2)), sep="")
cat(" BIC=", format(round(bic, 2)), "\n",sep="")
}
else cat("\nsigma^2 estimated as ", format(x$sigma2, digits=digits),
": part log likelihood=", format(round(x$loglik, 2)),
"\n", sep="")
invisible(x)
}
赞赏任何方向/建议。