在 R 中计算时间序列模型的准确性时出错（NextMethod(.Generic) 中的错误：(list) 对象不能强制键入 'double'）
Error while calculating accuracy of a timeseries model in R (Error in NextMethod(.Generic) : (list) object cannot be coerced to type 'double')
使用此代码：
#plotting time series from year 1998 to 2008
year.time_series <- ts(t_AMOUNT,start = c(1998) , frequency = 12 ) #Monthly 12
plot(year.time_series)
#splitting the timeseries for further model evaluation
train <- window(year.timeseries, start=1998,end=2005)
test <- window(year.timeseries, start=2005, end=2008)
#using models to check the accuracy results
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)
我在 accuracy(forecast.ets,test) 收到以下错误：
Error in NextMethod(.Generic) :
(list) object cannot be coerced to type 'double'
In addition: Warning message:
In !=.default(actual, predicted) :
longer object length is not a multiple of shorter object length
有没有办法拆分时间序列并计算其准确性？
问题是您使用的是 Metrics::accuracy() 而不是 forecast::accuracy()，这是可以完成我认为您想要的功能的函数。在解释了原因之后，我还提供了一些关于在 Stack Overflow 上提问的一般说明，如果您以后对本网站有其他问题，可能会对您有所帮助。
Metrics::accuracy() 对比 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 numerical vector containing forecasts...." 而 x 是 "An optional numerical 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，将预测作为第一个参数传递 class 对象，第二个是实际值的向量。
如果你想使用Metrics::accuracy()，它的参数就像
accuracy(actual, predicted)
其中 actual 是 "The ground truth vector, where elements of the vector can be any variable type" 而 predicted 是 "The predicted vector, where elements of the vector represent a prediction for the corresponding value in actual." 换句话说，您的第一个参数必须是 only 预测本身，而不是 forecast 对象中存在的所有其他信息。我也不认为它可以为您提供此类分析所需的准确度指标类型；它给出 "the proportion of elements in actual that are equal to the corresponding element in predicted".
以后提问的一些建议
首先，我会查看很棒的资源 How to make a great R reproducible example。接下来，我会给你我用来重现你的问题的代码，你会看到我必须做的一些改变才能开始（我的评论以 ### 开头）：
#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)
使用此代码：
#plotting time series from year 1998 to 2008
year.time_series <- ts(t_AMOUNT,start = c(1998) , frequency = 12 ) #Monthly 12
plot(year.time_series)
#splitting the timeseries for further model evaluation
train <- window(year.timeseries, start=1998,end=2005)
test <- window(year.timeseries, start=2005, end=2008)
#using models to check the accuracy results
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)
我在 accuracy(forecast.ets,test) 收到以下错误：
Error in NextMethod(.Generic) :
(list) object cannot be coerced to type 'double'In addition: Warning message:
In!=.default(actual, predicted) :
longer object length is not a multiple of shorter object length
有没有办法拆分时间序列并计算其准确性？
问题是您使用的是 Metrics::accuracy() 而不是 forecast::accuracy()，这是可以完成我认为您想要的功能的函数。在解释了原因之后，我还提供了一些关于在 Stack Overflow 上提问的一般说明，如果您以后对本网站有其他问题，可能会对您有所帮助。
Metrics::accuracy() 对比 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 numerical vector containing forecasts...." 而 x 是 "An optional numerical 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，将预测作为第一个参数传递 class 对象，第二个是实际值的向量。
如果你想使用Metrics::accuracy()，它的参数就像
accuracy(actual, predicted)
其中 actual 是 "The ground truth vector, where elements of the vector can be any variable type" 而 predicted 是 "The predicted vector, where elements of the vector represent a prediction for the corresponding value in actual." 换句话说，您的第一个参数必须是 only 预测本身，而不是 forecast 对象中存在的所有其他信息。我也不认为它可以为您提供此类分析所需的准确度指标类型；它给出 "the proportion of elements in actual that are equal to the corresponding element in predicted".
以后提问的一些建议
首先，我会查看很棒的资源 How to make a great R reproducible example。接下来，我会给你我用来重现你的问题的代码，你会看到我必须做的一些改变才能开始（我的评论以 ### 开头）：
#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)