Question

我是ARIMA与外部回归量预测和使用领域的新手。我试图根据历史数据预测第二天的客户需求。我每天有337个历史。这是一些代码：

modelFrequency=7
YearlyFrequency=84 #for multi seasonality
dataDuration = 1
futureHorizon = 1
testDataObs=67 #20% observations for testcase
minDateActuals =  "2017-02-27"
trainDateActuals = "2017-11-23"
maxDateActuals = "2018-01-29"
actualsAvg=rnorm(337,mean=100,sd=30) #for trial purpose.
actualsTS=zoo(actualsAvg$Demand,seq(from=as.Date(minDateActuals),to=as.Date(maxDateActuals),by = dataDuration),frequency = modelFrequency)
actualsTS = ts(actualsTS)
actualsMSTS = msts(actualsTS,seasonal.periods = c(modelFrequency,YearlyFrequency))

train = zoo(actualsTS,seq(from=as.Date(minDateActuals),to=as.Date(trainEndDate),by = dataDuration))
train = ts(train,frequency = modelFrequency)
trainMSTS = msts(train,seasonal.periods =  c(modelFrequency,YearlyFrequency))


aic_vals_temp=NULL #Trying to find a model with min. AIC
aic_vals=NULL
for (i in 1:3){
  for (j in 1:6){
     xreg1<-fourier(trainMSTS,K=c(i,j))
     fitma1<-auto.arima(trainMSTS,xreg=xreg1,seasonal = FALSE)
     aic_vals_temp<-cbind(i,j,fitma1$aic)
     aic_vals<-rbind(aic_vals,aic_vals_temp)
     print(aic_vals_temp)
    }
}

colnames(aic_vals) = c("Fourier7","Fourier84","AICValues")
aic_vals=data.frame(aic_vals)
minAICVal = min(aic_vals$AICValues)
minVals = aic_vals[which(aic_vals$AICValues == minAICVal),]
minVals=minVals[1,]
xregTrain=fourier(trainMSTS,K=c(minVals$Fourier7,minVals$Fourier84))
armaFourierTrain = auto.arima(trainMSTS,xreg=xregTrain,seasonal = FALSE)

xregTest=fourier(trainMSTS,K=c(minVals$Fourier7,minVals$Fourier84),h=testDataObs)
armaFourierTest =forecast(armaFourierTrain,xreg=xregTest, h=testDataObs)

 #works very well till this point on test data set. 
 #I will evaluate the errors on this model and compare it with another model's performance on test set and 
 #use one of the model to project next day demand. 
 #Now I want to use all available data to forecast next day.

actualsAvailable=zoo(actualsTS, seq(from = as.Date(minDateActuals), to = as.Date(maxDateActuals), by = dataDuration))
actualsAvailableTS=ts(actualsAvailable,frequency = modelFrequency)
actualsAvailableMSTS =msts(actualsAvailable,seasonal.periods = c(modelFrequency,YearlyFrequency))
aic_vals_temp=NULL
aic_vals=NULL
for (i in 1:3){
  for (j in 1:6){
    xreg1<-fourier(actualsAvailableMSTS,K=c(i,j))
    #xreg2<-fourier(trainMSTS,K=c(j))
    #xtrain<-cbind(xreg1,xreg2)
    fitma1<-auto.arima(actualsAvailableMSTS,xreg=xreg1,seasonal = FALSE)
    aic_vals_temp<-cbind(i,j,fitma1$aic)
    aic_vals<-rbind(aic_vals,aic_vals_temp)
    print(aic_vals_temp)
  }
}

colnames(aic_vals) = c("Fourier7","Fourier84","AICValues")
aic_vals=data.frame(aic_vals)
minAICVal = min(aic_vals$AICValues)
minVals = aic_vals[which(aic_vals$AICValues == minAICVal),]       
xregAllData = fourier(actualsAvailableMSTS,K=c(minVals$Fourier7,minVals$Fourier84),h = futureHorizon)    
   S1-7       C1-7     S1-84     C1-84
[1,] 0.9749279 -0.2225209 0.1490423 0.9888308
armaFourierAllData =auto.arima(actualsAvailableMSTS,xreg=data.frame(xregAllData),seasonal = FALSE)  #Problem is here.

Error in rowSums(xregg) : 'x' must be an array of at least two dimensions

使用类似方法对测试数据预测效果很好。 xregAllData的维度= [1,1：4]。那么我做错了什么。我将不胜感激任何帮助/见解/解释。请原谅上述代码中的任何拼写错误。

Answer 1

forecast :: auto.arima（）调用stats :: arima（）会删除所有均为零的xreg矩阵列。此操作会在随机位置导致各种错误，包括：

'x'必须是至少二维的数组
xreg等级不足

可能的解决方案是

添加更多数据
修改代码以排除传递包含全零的xreg列

使用xreg回归量预测ARIMA模型，提前1天面临问题

1 个答案: