使用“tslm”返回维度错误进行预测

Question

我遇到了与这里的提问者使用线性模型预测函数类似的问题，但我正在尝试使用 Rob Hyndman 预测包中的“时间序列线性模型”函数。

Predict.lm in R fails to recognize newdata

predict.lm with 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)

如您所见，我试图通过创建时间序列对象的数据框来避免名称问题。

但是，我随后拟合 tslm 对象（时间序列线性模型）如下 -

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

尽管创建了一个数据框并正确命名了所有对象，但我得到了与其他用户遇到的相同的维度错误。

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怎么回事：-

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

任何人都可以对我的模型规范提出任何其他可能有助于预测功能运行的改进建议吗？

编辑 1：好的，只是有一个工作示例，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，然后拟合了 tslm。我得到同样的错误（显然不同的尺寸）：-

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示例中使用的相同......

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：即使我将两个系列合并到一个数据框中，也会出现同样的错误。

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

Answer 1

tslm 拟合线性回归模型。如果要进行预测，则需要提供解释变量的未来值。这些应该通过forecast.lm 的newdata 参数提供。