R Forecast包中的Auto.Arima（）行为不稳定

我正在使用Rob Hyndman的forecast version 5.4插件。这是一个非常好的软件包，但它似乎表现得很奇怪，对相似数据的预测结果大不相同。我很确定这与数据末尾生成的警告消息有关，但我不确定如何修复它

     library(forecast)
     v <- vector("numeric")
     v <- append(v,0.0)
     v <- append(v,115.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,115.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,115.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,117.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,117.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,117.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,113.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,112.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,120.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,119.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     series <- ts(v, frequency=12)
     series
  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1   0 115   0   0   0 115   0   0   0 115   0   0
2   0   0 117   0   0   0 117   0   0   0 117   0
3   0   0   0   0 113   0   0   0 112   0   0   0
4   0   0   0   0 120   0   0   0 119   0   0   0
     a <- auto.arima(series)
     // Note there is no error

     v <- vector("numeric")
     v <- append(v,0.0)
     v <- append(v,109.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,120.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,114.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,125.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,135.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,130.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,104.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,114.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,126.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,114.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     v <- append(v,0.0)
     series <- ts(v, frequency=12)
     a <- auto.arima(series)
Warning message:
In max(which(abs(testvec)          1e-08)) :
  no non-missing arguments to max; returning -Inf
     series
  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1   0 109   0   0   0 120   0   0   0 114   0   0
2   0   0 125   0   0   0 135   0   0   0 130   0
3   0   0   0   0 104   0   0   0 114   0   0   0
4   0   0   0   0 126   0   0   0 114   0   0   0

库（预测）
v这是您的数据，输入效率更高。为什么要使用
append
语句

library(forecast)
v1 <- ts(c(0, 115, 0, 0, 0, 115, 0, 0, 0, 115, 0, 0, 0, 0, 117, 0, 0, 
          0, 117, 0, 0, 0, 117, 0, 0, 0, 0, 0, 113, 0, 0, 0, 112, 0, 0, 
          0, 0, 0, 0, 0, 120, 0, 0, 0, 119, 0, 0, 0), frequency=12)
fit1 <- auto.arima(v1)
plot(forecast(fit1))

v2 <- ts(c(0, 109, 0, 0, 0, 120, 0, 0, 0, 114, 0, 0, 0, 0, 125, 0, 0, 
       0, 135, 0, 0, 0, 130, 0, 0, 0, 0, 0, 104, 0, 0, 0, 114, 0, 0, 
       0, 0, 0, 0, 0, 126, 0, 0, 0, 114, 0, 0, 0), frequency=12)
fit2 <- auto.arima(v2)
plot(forecast(fit2))

库（预测）
v1谁教你使用append创建向量，每次只添加一个值？非常感谢建设性的批评或指点。也许教授正确的创建向量的方法更合适？谢谢Rob。我以为这是因为一个背景被引爆了。感谢您提供了创建向量的正确方法。是否记录了这种情况下
auto.arima
的行为？从答案中显示的行为来看，预测结果似乎只是结转的序列的平均值，这很好。但我需要对自己对
auto.arima
如何在不合适的时间序列上工作的理解充满信心。
library(forecast)
v1 <- ts(c(0, 115, 0, 0, 0, 115, 0, 0, 0, 115, 0, 0, 0, 0, 117, 0, 0, 
          0, 117, 0, 0, 0, 117, 0, 0, 0, 0, 0, 113, 0, 0, 0, 112, 0, 0, 
          0, 0, 0, 0, 0, 120, 0, 0, 0, 119, 0, 0, 0), frequency=12)
fit1 <- auto.arima(v1)
plot(forecast(fit1))

v2 <- ts(c(0, 109, 0, 0, 0, 120, 0, 0, 0, 114, 0, 0, 0, 0, 125, 0, 0, 
       0, 135, 0, 0, 0, 130, 0, 0, 0, 0, 0, 104, 0, 0, 0, 114, 0, 0, 
       0, 0, 0, 0, 0, 126, 0, 0, 0, 114, 0, 0, 0), frequency=12)
fit2 <- auto.arima(v2)
plot(forecast(fit2))