ARIMA错误

我试图在.csv文件中的临时数据集上运行ARIMA。以下是我目前的代码：

Oil_all <- read.delim("/Users/Jkels/Documents/Introduction to Computational
Statistics/Oil production.csv",sep="\t",header=TRUE,stringsAsFactors=FALSE)
Oil_all

代码：

结果:

[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

当我运行ARIMA时：

library(forecast)
auto.arima(Oil_all,xreg=year)

这就是错误：

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
In addition: Warning message:
In data.matrix(data) : NAs introduced by coercion

因此，我能够调用数据集并打印出来。但是，当我检查apply函数是否存在这些值时，我看到了所有的0，因此我知道有什么地方出错，这可能就是我出错的原因。我只是不确定这个错误是什么意思，或者如何在代码中修复它

---

有什么建议吗？

如果我没弄错你的问题，应该是这样的：

Oil_all <- read.csv("myfolder/myfile.csv",header=TRUE) 
## I don't have your source data, so I tried to reproduce it with the data you printed
Oil_all
   year value
1  1880    30
2  1890    77
3  1900   149
4  1905   215
5  1910   328
6  1915   432
7  1920   689
8  1925  1069
9  1930  1412
10 1935  1655
11 1940  2150
12 1945  2595
13 1950  3803
14 1955  5626
15 1960  7674
16 1962  8882
17 1964 10310
18 1966 12016
19 1968 14104
20 1970 16690
21 1972 18584
22 1974 20389
23 1976 20188
24 1978 21922
25 1980 21732
26 1982 19403
27 1984 19608


library(forecast)
auto.arima(Oil_all$value,xreg=Oil_all$year)

Series: Oil_all$value 
ARIMA(3,0,0) with non-zero mean 

Coefficients:
         ar1     ar2      ar3  intercept  Oil_all$year
      1.2877  0.0902  -0.4619  -271708.4      144.2727
s.e.  0.1972  0.3897   0.2275   107344.4       55.2108

sigma^2 estimated as 642315:  log likelihood=-221.07
AIC=454.15   AICc=458.35   BIC=461.92

Oil\u all您的导入应该是

Oil_all<-read.csv("/Users/Jkels/Documents/Introduction to Computational Statistics/Oil production.csv")

Oil_all@Nemesi您好，所以我尝试了您的建议，出于某种原因，我现在遇到了一个新错误：ts（x）中的错误：'ts'对象必须有一个或多个观测点谢谢，我找到了！嗨，Elle，一般来说，这是一个建议：如果你提供你正在使用的数据，回答你问题的人可以给你更准确的建议。很高兴听到你成功了！非常感谢。我知道了！
Oil_all <- read.csv("myfolder/myfile.csv",header=TRUE) 
## I don't have your source data, so I tried to reproduce it with the data you printed
Oil_all
   year value
1  1880    30
2  1890    77
3  1900   149
4  1905   215
5  1910   328
6  1915   432
7  1920   689
8  1925  1069
9  1930  1412
10 1935  1655
11 1940  2150
12 1945  2595
13 1950  3803
14 1955  5626
15 1960  7674
16 1962  8882
17 1964 10310
18 1966 12016
19 1968 14104
20 1970 16690
21 1972 18584
22 1974 20389
23 1976 20188
24 1978 21922
25 1980 21732
26 1982 19403
27 1984 19608


library(forecast)
auto.arima(Oil_all$value,xreg=Oil_all$year)

Series: Oil_all$value 
ARIMA(3,0,0) with non-zero mean 

Coefficients:
         ar1     ar2      ar3  intercept  Oil_all$year
      1.2877  0.0902  -0.4619  -271708.4      144.2727
s.e.  0.1972  0.3897   0.2275   107344.4       55.2108

sigma^2 estimated as 642315:  log likelihood=-221.07
AIC=454.15   AICc=458.35   BIC=461.92

Oil_all<-read.csv("/Users/Jkels/Documents/Introduction to Computational Statistics/Oil production.csv")