问题是你使用的是Metrics::accuracy()而不是forecast::accuracy()，这是我认为你想要的功能。在解释了原因之后，我也有一些关于在Stack Overflow上提问的一般性说明，如果你将来对这个网站有其他问题，可能会对你有帮助。

Metrics::accuracy() vs. forecast::accuracy()

如果我们查看帮助文件（help("forecast::accuracy")和help("Metrics::accuracy")），我们可以看到函数之间的一些差异。
预测准确性的论据如下

accuracy(f, x, test = NULL, d = NULL, D = NULL, ...)

其中f是“An object of class“forecast”，or a numeric vector containing forecasts....”，x是“An optional numeric vector containing actual values of the same length as object，or a time series overlapping with the times of f.”这与您尝试使用它的方式相匹配，将forecast class对象作为第一个参数传递，将实际值的向量作为第二个参数传递。
如果你想使用Metrics::accuracy()，它的参数如下：

accuracy(actual, predicted)

其中actual是“真实向量，其中向量的元素可以是任何变量类型”，predicted是“预测向量，其中向量的元素表示实际对应值的预测”。换句话说，您的第一个参数必须是only预测本身，并不是所有的其他信息都存在于一个forecast对象中。我也不认为它能提供你想要的这种分析的准确性度量;它给出了“实际中的元素与预测中的相应元素相等的比例”。

对以后提问的建议

首先，我会查看很棒的资源How to make a great R reproducible example。接下来，我会给予你我用来重现你的问题的代码，你会看到我必须做的一些修改（我的评论以###开始）：

#plotting time series from year 1998 to 2008 
### Since we don't have t_AMOUNT, we can't recreate your data
# year.time_series <- ts(t_AMOUNT, start = c(1998), frequency = 12) #Monthly 12
### So I did the following to make some dummy data
set.seed(42)
year.time_series <- ts(rnorm(12*11), start = c(1998), frequency = 12 )
plot(year.time_series)
#splitting the timeseries for further model evaluation
### Since there are spelling changes below for some reason,
### I had to do the next line (or change the variable names below)
year.timeseries <- year.time_series 
train <- window(year.timeseries, start=1998, end=2005)
test <- window(year.timeseries, start=2005, end=2008)

#using models to check the accuracy results
### We need the forecast library for ets(),
### but it wasn't loaded in your code
library(forecast) 
etsfit <- ets(train)
summary(etsfit)

plot(train, main = "ETS Forecast", ylab = "ets(training set)",
     cex.lab = 1.5, cex.main = 1.5, cex.axis = 1.5)
lines(etsfit$fitted, col = "orange")

#forecast
forecast.ets <- forecast(etsfit, h = 24)
summary(forecast.ets)
plot(forecast.ets)

plot(forecast.ets, main = "2 Year Forecast Using ETS Model",
     xlim = c(1998, 2008), cex.lab = 1.5, cex.main = 1.5, cex.axis = 1.5)
lines(test, col = "red")

library(Metrics)
#input = forecast values, actual values
accuracy(forecast.ets,test)
forecast::accuracy(forecast.ets, test)