为什么在R编程中运行Accurracy函数时出错？

问题描述

我正在尝试使用r编程中的精度函数来寻找预测模型的精度。但是我在R Studio中收到以下错误。我是否需要更改预测变量的模式或其他内容？我已经将类视为ts并将mode视为数值。

我不明白为什么，我们将不胜感激。下面是完整的代码。

dput结果：

dput(Enrollment_Data)
structure(list(COUNT = c(17L,1L,5L,8L,45L,21L,18L,43L,82L,116L,192L,289L,242L,254L,335L,138L,71L,98L,91L,175L,232L,155L,376L,197L,271L,421L),Enrolment_date = structure(c(25L,20L,10L,16L,14L,12L,3L,26L,23L,6L,11L,9L,17L,2L,19L,15L,13L,4L,27L,24L,22L,7L),.Label = c("APR2018","APR2019","AUG2018","AUG2019","DEC2017","DEC2018","DEC2019","FEB2018","FEB2019","JAN2018","JAN2019","JUL2018","JUL2019","JUN2018","JUN2019","MAR2018","MAR2019","MAY2018","MAY2019","NOV2017","NOV2018","NOV2019","OCT2018","OCT2019","SEP2017","SEP2018","SEP2019"),class = "factor")),class = "data.frame",row.names = c(NA,-27L))

数据：

 COUNT Enrolment_date
1     17        SEP2017
2      1        NOV2017
3      5        DEC2017
4      8        JAN2018
5     45        FEB2018
6     21        MAR2018
7     18        APR2018
8     43        MAY2018
9     82        JUN2018
10   116        JUL2018
11   192        AUG2018
12   289        SEP2018
13   242        OCT2018
14   254        NOV2018
15   335        DEC2018
16   138        JAN2019
17    71        FEB2019
18    98        MAR2019
19    91        APR2019
20   138        MAY2019
21   175        JUN2019
22   232        JUL2019
23   155        AUG2019
24   376        SEP2019
25   197        OCT2019
26   271        NOV2019
27   421        DEC2019

错误：

> accuracy(forecast1)
Error in accuracy.default(forecast1) : 
  First argument should be a forecast object or a time series.

完整代码：

Enrollment_Data <- read.csv('RateT0.csv')

head(Enrollment_Data)

#load packages
library(tseries)
library(ggplot2)
library(forecast)


attach(Enrollment_Data)
#Data Exploration

plot(COUNT)
#Forecasting Returns

adf.test(COUNT,alternative="stationary")

d.COUNT <- diff(COUNT,differences = 3)
summary(COUNT)
summary(d.COUNT)

plot(d.COUNT)

adf.test(d.COUNT,alternative="stationary")

acf(d.COUNT)
pacf(d.COUNT)

#Step 2: Model Estimation

forecast::auto.arima(d.COUNT)

#Step 4: Diagnosis
arima.final <-arima(d.COUNT,c(3,0))

tsdiag(arima.final)

'Choose the one that has least AIC and significant co-efficients'

#arima.final <-arima(COUNT,3,1))

forecast1 <- predict(arima.final,n.ahead = 12)
forecast1

summary(forecast1)
accuracy(forecast1)
plot(d.COUNT)

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。

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