forecast（auto.arima）：match.fun（fun）：节点堆栈溢出

在花时间阅读和实施解决方案后，我仍然无法找出下面错误消息的原因，希望得到一些反馈

• 作者的源代码：（可以跳过平稳性测试）

我关注的是：

fcast.arima <- forecast(fit.arima, h, lambda=L) -> Error in match.fun(FUN) : node stack overflow

确实有效，所以肯定有一些怪事发生在RARIMA身上

最好的

以下是清理后的代码：

库（Quandl）
图书馆（tseries）
图书馆（预测）
你能举一个可重复的例子吗？@DatamineR:当然。与线程中提到的链接相同[@DatamineR:thread也进行了编辑，以防有所帮助。
fcast.arima
workst for me for me with the anyerros@DatamineR：幸运的是…同时很高兴阅读它，因为它纯粹证明我的电脑出了问题，而不是代码中的问题。如前所述，我看不出语法有任何问题
fcast.nn <- forecast(fit.nn, h, lambda=L)

library(Quandl)
library(tseries)
library(forecast)

oil.ts <- Quandl("DOE/RBRTE", trim_start="1987-11-10", trim_end="2015-01-01", type="zoo")
oil.tsw <-Quandl("DOE/RBRTE", trim_start="1987-11-10", trim_end="2015-01-01", type="zoo", collapse="weekly")
oil.tsm <-Quandl("DOE/RBRTE", trim_start="1987-11-10", trim_end="2015-01-01", type="ts", collapse="monthly")
plot(oil.tsm, xlab="Year", ylab="Price, $", type="l")
lines(lowess(oil.tsm), col="red", lty="dashed")

L <- BoxCox.lambda(ts(oil.ts, frequency=260), method="loglik")
Lw <- BoxCox.lambda(ts(oil.tsw, frequency=52), method="loglik")
Lm <- BoxCox.lambda(oil.tsm, method="loglik")

# Fit NN for long-run
fit.nn <- nnetar(ts(oil.ts, frequency=260), lambda=L, size=3)
fcast.nn <- forecast(fit.nn, h=520, lambda=L)
fit.nnw <- nnetar(ts(oil.tsw, frequency=52), lambda=Lw, size=3)
fcast.nnw <- forecast(fit.nnw, h=104, lambda=Lw)
fit.nnm <- nnetar(oil.tsm, lambda=Lm, size=3)
fcast.nnm <- forecast(fit.nnm, h=24, lambda=Lm)
par(mfrow=c(3, 1))
plot(fcast.nn, include=1040)
plot(fcast.nnw, include=208)
plot(fcast.nnm, include=48)

# Fit ARIMA, NN and ETS for short-run
short <- ts(oil.ts[index(oil.ts) > "2014-06-30" & index(oil.ts) < "2014-12-  01"], frequency=20)
short.test <- as.numeric(oil.ts[index(oil.ts) >= "2014-12-01",])
h <- length(short.test)
fit.arima <- auto.arima(short, lambda=L)
fcast.arima <- forecast(fit.arima, h, lambda=L)
fit.nn <- nnetar(short, size=7, lambda=L)
fcast.nn <- forecast(fit.nn, h, lambda=L)
fit.tbats <-tbats(short, lambda=L)
fcast.tbats <- forecast(fit.tbats, h, lambda=L)
par(mfrow=c(3, 1))
plot(fcast.arima, include=3*h)
plot(fcast.nn, include=3*h)
plot(fcast.tbats, include=3*h)