Question

我遇到过与线性模型预测函数相似的问题，但我正在尝试使用Rob Hyndman的预测包中的“时间序列线性模型”函数。

Predict.lm in R fails to recognize newdata

totalConv <- ts(varData[,43])
metaSearch <- ts(varData[,45])
PPCBrand <- ts(varData[,38])
PPCGeneric <- ts(varData[,34])
PPCLocation <- ts(varData[,35])
brandDisplay <- ts(varData[,29])
standardDisplay <- ts(varData[,3])
TV <- ts(varData[,2])
richMedia <- ts(varData[,46])

df.HA <- data.frame(totalConv, metaSearch,  
            PPCBrand, PPCGeneric, PPCLocation,
            brandDisplay, standardDisplay, 
            TV, richMedia)

正如您所看到的，我试图通过创建时间序列对象的数据框来避免名称问题。

然而，我然后拟合tslm对象（时间序列线性模型），如下所示 -

fit1 <- tslm(totalConv ~ metaSearch  
             + PPCBrand + PPCGeneric + PPCLocation 
             + brandDisplay + standardDisplay 
             + TV + richMedia data = df.HA
             )

尽管已经创建了一个数据框并且正确地命名了所有对象，但我得到了与其他用户相同的尺寸错误。

Error in forecast.lm(fit1) : Variables not found in newdata
In addition: Warning messages:
1: 'newdata' had 10 rows but variables found have 696 rows 
2: 'newdata' had 10 rows but variables found have 696 rows

模型框架似乎为所有变量提供了合理的名称，所以我不知道预测函数是什么： -

names(model.frame(fit1))
[1] "totalConv"       "metaSearch"      "PPCBrand"        "PPCGeneric"      "PPCLocation"     "brandDisplay"   
[7] "standardDisplay" "TV"              "richMedia"

有人可以建议我的模型规范的任何其他改进可能有助于预测功能运行吗？

编辑1：好的，这是一个有效的例子，我使用了Irsal对这个问题的回答（转换为时间序列对象）中给出的数据，然后安装了tslm。我得到了同样的错误（显然是不同的尺寸）： -

Is there an easy way to revert a forecast back into a time series for plotting?

我真的很困惑我做错了什么，我的代码看起来与所有这些例子中的代码相同....

data <- c(11,53,50,53,57,69,70,65,64,66,66,64,61,65,69,61,67,71,74,71,77,75,85,88,95,
           93,96,89,95,98,110,134,127,132,107,94,79,72,68,72,70,66,62,62,60,59,61,67,
           74,87,112,134,51,50,38,40,44,54,52,51,48,50,49,49,48,57,52,53,50,50,55,50,
           55,60,65,67,75,66,65,65,69,72,93,137,125,110,93,72,61,55,51,52,50,46,46,45,
           48,44,45,53,55,65,89,112,38,7,39,35,37,41,51,53,57,52,57,51,52,49,48,48,51,
           54,48,50,50,53,56,64,71,74,66,69,71,75,84,93,107,111,112,90,75,62,53,51,52,
           51,49,48,49,52,50,50,59,58,69,95,148,49,83,40,40,40,53,57,54,52,56,53,55,
           55,51,54,45,49,46,52,49,50,57,58,63,73,66,63,72,72,71,77,105,97,104,85,73,
           66,55,52,50,52,48,48,46,48,53,49,58,56,72,84,124,76,4,40,39,36,38,48,55,49,
           51,48,46,46,47,44,44,45,43,48,46,45,50,50,56,62,53,62,63)

 data2 <- c(rnorm(237))


library(forecast)



 nData <- ts(data)
 nData2 <- ts(data2)
 dat.ts <- tslm(nData~nData2)
 forecast(dat.ts)
Error in forecast.lm(dat.ts) : Variables not found in newdata
In addition: Warning messages:
1: 'newdata' had 10 rows but variables found have 237 rows 
2: 'newdata' had 10 rows but variables found have 237 rows

编辑2：即使我将两个系列组合成数据帧，也会出现相同的错误。

nData.df <- data.frame(nData, nData2)
dat.ts <- tslm(nData~nData2, data = nData.df)
forecast(dat.ts)

Answer 1

tslm符合线性回归模型。如果要进行预测，则需要提供解释变量的未来值。这些应通过newdata的{{1}}参数提供。

使用`tslm`返回维度错误进行预测

1 个答案: