Question

对于上下文，我是新手R用户，所以请原谅任何不正确的术语/流程。我正在积极努力提高我的编码能力，但最近却变得难堪。

我有以下数据集，其中A * B * C =输出：

日期A B C输出
  1/1/2013 177352 0.908329198 0.237047935 38187
  1/2/2013 240724 0.852033865 0.237273592 48666
  1/3/2013 243932 0.908380204 0.237039845 52524
  1/4/2013 221485 0.820543152 0.236356733 42955
  1/5/2013 202590 0.818066045 0.240900973 39925
  1/6/2013 238038 0.770057722 0.247344561 45339
  1/7/2013 271511 0.794258796 0.241252029 52026
  1/8/2013 283434 0.807817693 0.233810703 53534
  1/9/2013 275016 0.843220031 0.243769917 56530
  1/10/2013 255266 0.797791324 0.238562428 48583
  1/11/2013 226564 0.815791564 0.236153417 43648
  1/12/2013 214366 0.800066242 0.237961133 40812
  1/13/2013 256946 0.764845532 0.237640186 46702
  1/14/2013 282298 0.816537843 0.234257528 53998

我有几年的数据，我正在尝试使用A，B和C预测输出。但是，当我单独模拟A，B和C时，输出变得非常偏斜。如果我只预测输出，那么我会丢失输入因子。

完成此任务的最佳包/代码是什么？我已经尝试使用谷歌搜索并在这里搜索了许多不同的方法，但还没有找到我正在寻找的解决方案。

以下是一些代码：

DataSet1[,"Date"] <- mdy(DataSet[,"Date"])

DataSet1
TotalSet <- ts(DataSet1, frequency = 365, start =c(2013,1))
DataA <- ts(DataSet1$A, frequency = 365, start = c(2013,1))
DataB <- ts(DataSet1$B, frequency = 365, start = c(2013,1))
DataC <- ts(DataSet1$C, frequency = 365, start = c(2013,1))
OutputData <- ts(DataSet$Output, frequency = 365, start = c(2013,1))

ADecompose <- decompose(DataA)
BDecompose <- decompose(DataB)
CDecompose <- decompose(DataC)
OutputDecompose <- decompose(OutputData)

DataAHW <- HoltWinters(DataA, seasonal = "mult")
DataBHW <- HoltWinters(DataB, seasonal = "mult")
DataCHW <- HoltWinters(DataC, seasonal = "mult")
OutputDataHW <- HoltWinters(OutputData, seasonal = "mult")

FC.A <- forecast.HoltWinters(DataAHW)
FC.B <- forecast.HoltWinters(DataBHW)
FC.C <- forecast.HoltWinters(DataCHW)
FC.Output <- forecast.HoltWinters(OutputDataHW)

plot(ForecastVisits)
plot(ForecastCPV)
plot(ForecastRPC)
plot(ForecastRevenue)

这是我为Output构建的另一个模型，我将A，B和C单独插入其中，然后将它们组合在excel中。我确信有一种更合适的方法可以解决这个问题，但鉴于我缺乏经验，我正在寻求帮助

dataset <- testData


##FORECAST

forecastingFuntion <- function(dataset, lenghtOfForecast)

  {
dataset[,"Date"] <- mdy(dataset[,"Date"])
myts <- ts(dataset[,"DataSet$Output"], start = c(2013,1), frequency = 365)

hwModel <- HoltWinters(myts, seasonal = "mult")

future <- data.frame(predict(hwModel, n.ahead = lenghtOfForecast, level =       0.9))
fittedValues <- data.frame(as.numeric(hwModel$fitted[,"xhat"]))
names(fittedValues) <- "fit"

futureDates <- c()
predicitedValues <- rbind(fittedValues, future)

for(i in 1: lenghtOfForecast)
  {
  futureDateSingle <- data.frame(dataset[nrow(dataset),"Date"] + days(i))
  futureDates <- rbind(futureDates, futureDateSingle)  
  }  

names(futureDates) <- "Date"
dates <- data.frame(dataset[366:(nrow(dataset)),"Date"])
names(dates) <- "Date"
dates <- rbind(dates, futureDates)
predictedData <- data.frame(predicitedValues, dates)
names(predictedData) <- c("predictedValues","Date")
finalData2 <- mergeData <- merge(predictedData, dataset, all.x = T, all.y = F, by = "Date")
finalData2
}



finalData2 <- forecastingFuntion(testData, 612)

rm(list=setdiff(ls(), c("finalData2")))

write.csv(finalData2, file="B2BForecastVisits.csv")

谢谢！

具有多个输入变量的Holt Winters预测

0 个答案: