我已经编写了一个代码，该代码适用于同比季度百分比增长。但是，我的代码仅适用于我用来编写代码的数据。我希望能够使用不同长度的数据运行我的整个代码，而不必更改任何内容。
这是我的代码：
>lastyr<-tail(datan,horiz) #selects the last values from the original data
>percentf<-((Arimab2f/lastyr)-1)*100 #finds the percentage growth of the forecasted data
>percentfr<-round(percentf,digits=2) #rounds the results to 3d.p
>percentf_percent<-paste(percentfr,"%",sep="")
>plot.ts(percentf,xaxt="n",ylab="Percentage growth rate",xlab="Quarter",main="Percentage Growth of the Forecasts") #plots the percentage growth of the forecasts and removes the x-axis values
>axis(1,at=seq(1,horiz,by=1),las=1) #adds the choosen values onto the x-axis at the points
>points(percentf,col="blue") #puts a blue circle around each of the points
>for(i in 1:length(percentfr))
+{text(x=i,y=percentfr[i],labels=percentf_percent[i],cex=0.5,pos=1)
+text(x=i,y=percentfr[i],labels=percentf_percent[i],cex=0.5,pos=3)} #adds the percentage values next to the points on both the left and right side
>fulldata<-c(datan,Arimab2f,0,0) #this makes a vector of all the data with the forecasts, the added zeros make it easier to seperate the years if it doesnt finish in the 4th quarter
>fullmat<-matrix(fulldata,nrow=freqdata) #produces a matrix of the full data with the years seperated into columns
>full1mat<-fullmat[,-1] #removes the first column from the matrix
>full2mat<-matrix(c(full1mat,0,0,0,0),nrow=freqdata) #makes a matrix with a zero column at the end to account for the one removed in the last line
>percent1<-((full2mat[,1]/fullmat[,1])-1)*100
>percent2<-((full2mat[,2]/fullmat[,2])-1)*100
>percent3<-((full2mat[,3]/fullmat[,3])-1)*100
>percent4<-((full2mat[,4]/fullmat[,4])-1)*100
>percent5<-((full2mat[,5]/fullmat[,5])-1)*100
>percent6<-((full2mat[,6]/fullmat[,6])-1)*100
>percent7<-((full2mat[,7]/fullmat[,7])-1)*100
>percent8<-((full2mat[,8]/fullmat[,8])-1)*100
#percent9<-((full2mat[,9]/fullmat[,9])-1)*100 #add as many percents as there is years in the data
#percent10<-((full2mat[,10]/fullmat[,10])-1)*100
#percent11<-((full2mat[,11]/fullmat[,11])-1)*100
#percent12<-((full2mat[,12]/fullmat[,12])-1)*100
#percent13<-((full2mat[,13]/fullmat[,13])-1)*100
#percent14<-((full2mat[,14]/fullmat[,14])-1)*100
>percentagegrowth<-c(percent1,percent2,percent3,percent4,percent5,percent6,percent7,percent8)#,percent9,percent10,percent11,percent12,percent13,percent14) #puts the percentage growths for each year in the same vector
>percentagegrowth1<-head(percentagegrowth,-(length(fullmat)-length(datan))) #removes the unnecessary values from the end of the matrix
>zero<-matrix(,nrow=(length(percentagegrowth1)-length(percentf))) #creates a matrix with no values
>percentf1<-c(zero,percentf) #creates a vector with the NA values and the percantage growth of the forecast data
>percentagegrowth1r<-round(percentagegrowth1,1)
>names(percentagegrowth1)<-c("09 q1","09 q2","09 q3","09 q4","10 q1","10 q2","10 q3","10 q4","11 q1","11 q2","11 q3","11 q4","12 q1","12 q2","12 q3","12 q4","13 q1","13 q2","13 q3","13 q4","14 q1","14 q2","14 q3","14 q4","15 q1","15 q2")
>percentagegrowth1_percent<-paste(percentagegrowth1r,"%",sep="")
>plot.ts(percentagegrowth1,xaxt="n",xlab="Quarter",ylab="Percentage growth(%)",main="Year-over-Year Quarterly Percentage Growth") #plots all the percentage growth points
>for(i in 1:length(percentagegrowth1r))
+{text(x=i,y=percentagegrowth1r[i],labels=percentagegrowth1_percent[i],cex=0.5,font=2,pos=1)
+ text(x=i,y=percentagegrowth1r[i],labels=percentagegrowth1_percent[i],cex=0.5,font=2,pos=3)}
>lines(percentf1,col="red") #adds the forecasted data as a red line
>points(percentagegrowth1r,col="blue") #circles each of the points making them easier to see
>axis(1,at=seq(1,length(percentagegrowth1),by=1),labels=names(percentagegrowth1),las=2,cex.axis=0.6)
>legend("topright",c("Red=Forecasted data"))
有什么方法可以缩短它有 percent1,percent2...#percent14 的代码？并且在名称（百分比增长1）中考虑到数据的长度可能会改变，因此名称会改变吗？
这是此代码中使用的数据，因此您可以看到我做了什么：
http://s21.postimg.org/t6nldfo13/datan.png (datan)
http://s14.postimg.org/vmn2kjatp/arimab2f.png（Arimab2f（使用 ARIMA 的预测数据））
水平=4
频率数据=4
您可以通过复制和粘贴所有这些（包括数据）来运行我的整个代码：
datan<-c(79160.56,91759.73,91186.48,106353.82,70346.47,80279.15,82611.60,131392.72,93798.99,105944.78,103913.13,154530.69,110157.40,117416.09,127423.42,156752.00,120097.81,121307.75,115021.12,150657.83,113711.53,115353.14,112701.98,154319.18,116803.54,118352.54)
freqdata<-4 #set frequency of the data (e.g. 4 for quarterly, 12 for monthly etc)
startdata<-c(8,1) #where the first interval is ((8,1) means first quarter in 2008)
horiz<-4 #how many predictions to be made (4 predicts a year for the quarterly data)
datats<-ts(datan,frequency=freqdata,start=startdata) #turns the data into a time series with the frequency of data and where it starts
plot(datats,ylab="Total",xlab="Time",main="Original Time Series Plot") #plots a time series graph of the original data
seasonplot(datats,ylab="Total",xlab="Time",main="Seasonal plot",year.labels=TRUE,year.labels.left=TRUE,col=1:20,pch=19)
fit<-stl(datats,s.window=5)
lines(fit$time.series[,1],col="red",ylab="Trend")
plot(fit)
force.log<-"log"
datadates<-as.character(data[,1]) #creates a character vector of the data column
dataMAT<-matrix(0,ncol=freqdata,nrow=(length(datats)+freqdata),byrow=TRUE) #creates a zero matrix of size specified and 'byrow=TRUE' specifies you want it to fill the matrix row-wise (column-wise is the default)
for(i in 1:freqdata)
{dataMAT[,i]<-c(rep(0,length=i-1),lag(datats,k=-i+1),rep(0,length=freqdata-i+1))} #for every i in 1 to freqdata creates a vector entry in the i column of the zero matrix. 'rep(0,length=i-1)' is 0 repeated i-1 times, 'lag(datan,k=-i+1)' shifts the time space of datan back by -i+1 observations
dataind<-dataMAT[c(-1:(-freqdata+1),-(length(dataMAT[,1])-freqdata+1):-(length(dataMAT[,1]))),] #fills in the zero matrix diagonally with the values from the above input
dataind2<-data.frame(dataind) #creates a data frame of the matrix making it easier for R
lm1<-lm(X1~.,data=dataind2) #creates a full linear model with x1 being the dependant variable using data from dataind2
lm2<-lm(X1~X2+dataind2[,length(dataind2[1,])],data=dataind2) #creates a linear model with dependant variable x1 with x2 and 'dataind2[,length(dataind2[1,])]' being variables in the model (includes 1 lag and 1y lag) using data from dataind2.
library(lmtest) #activates the lmtest package
library(car) #activates the car package
bptest1<-bptest(lm1) #does a Breusch-Pagan test on lm1 to test for heteroscedasticity (see http://en.wikipedia.org/wiki/Heteroscedasticity for description)
bptest2<-bptest(lm2) #does a Breusch-Pagan test on lm2 to test for heteroscedasticity
gqtest1<-gqtest(lm1) #does a Goldfeld-Quandt test on lm1 to test for homoscedasticity (if all random variable in model have the same finite variance)
ncvtest1<-ncvTest(lm1) #a non-constant error variance test on lm1 (much like the Breusch-Pagan). add depending on model
ncvtest2<-ncvTest(lm2) #a non-constant error variance test on lm2. add depending on model
if(force.log=="level")
{aslog<-"n"}else
{{if(force.log=="log")
{aslog<-"y"}else
{if(bptest1$p.value<0.1|bptest2$p.value<0.1|gqtest1$p.value<0.1|ncvtest1$p<0.1|ncvtest2$p<0.1) #if the p-value of bptest1/bptest2/gqtest1 is < 0.1 name aslog 'y'
{aslog<-"y"}else
{aslog<-"n"}}}}
if(aslog=="y")
{dataa<-log(datats)}else
{dataa<-datats} #if there is evidence to show that the data should be transformed then it makes a log of the data if not then it remains the same
startLa<-startdata[1]+trunc((1/freqdata)*(length(dataa)-horiz)) #finds a start year
startLb<-1+((1/freqdata)*(length(dataa)-horiz)-trunc((1/freqdata)*(length(dataa)-horiz)))*freqdata #finds a start quarter
startL<-c(startLa,startLb) #creates a vector of the date
K<-ts(rep(dataa,length=length(dataa)-horiz),frequency=freqdata,start=startdata) #split series into two, K is in sample (the original sample)
L<-ts(dataa[-1:-(length(dataa)-horiz)],frequency=freqdata,start=startL) #split series into two, L is out sample (predictions)
library(strucchange) #activates strucchange package
efp1rc<-efp(lm1,data=dataind2,type="Rec-CUSUM") #returns a one-dimensional empirical process of cumulative sums of residuals from lm1
efp2rc<-efp(lm2,data=dataind2,type="Rec-CUSUM") #returns a one-dimensional empirical process of cumulative sums of residuals from lm2
efp1rm<-efp(lm1,data=dataind2,type="Rec-MOSUM") #returns a one-dimensional empirical process of moving sums of residuals from lm1
efp2rm<-efp(lm2,data=dataind2,type="Rec-MOSUM") #returns a one-dimensional empirical process of moving sums of residuals from lm2
plot(efp2rc) #plots the recursive cumulative sum of residuals for lm2
lines(efp1rc$process,col ="darkblue") #plots the recursive cumulative sum of residuals for lm1 on the same graph
plot(efp2rm)
lines(efp1rm$process,col="darkblue")
gefp2<-gefp(lm2,data=dataind2) #plots a M-fluctuation of lm2
plot(gefp2)
plot(dataa) #plots a graph of dataa
pacf(dataa) #plots a correlogram of dataa
sctest(efp2rc) #tests for structural change in regression model
cat("log series,y/n?:",aslog) #shows if series has been logged or not
########## ARIMA ########
library(tseries) #activates tseries package
library(forecast) #activates forecast package
max.sdiff<-3 #set the maximum seasonal differences allowed
arima.force.seasonality<-"n"
kpssW<-kpss.test(dataa,null="Level") #computes the Kwiatkowski-Phillips-Schmidt-Shin test for the null-hypothesis that dataa is level
#kpssW<-ndiffs(dataa,alpha=0.05,test="kpss") #if the above doesn't work
ppW<-tryCatch({ppW<-pp.test(dataa,alternative="stationary")},error=function(ppW){ppW<-list(error="TRUE",p.value=0.99)}) #performs a Phillips-Perron Unit Root test for the null hypothesis that dataa has a unit root instead of a stationary alternative. if p.value>0.05 then assume unit root
adfW<-adf.test(dataa,alternative="stationary",k=trunc((length(dataa)-1)^(1/3))) #performs the Augmented Dickey-Fuller test that the null of dataa has unit root
if(kpssW$p.value<0.05|ppW$p.value>0.05|adfW$p.value>0.05)
{ndiffsW=1}else
{ndiffsW=0} #finds the estimate of the number of differences required to make time series stationary
aaW<-auto.arima(dataa,max.D=max.sdiff,d=ndiffsW,seasonal=TRUE,allowdrift=FALSE,stepwise=FALSE,trace=TRUE,seasonal.test="ch") #fits the best ARIMA model
orderWA<-c(aaW$arma[1],aaW$arma[6],aaW$arma[2])
orderWS<-c(aaW$arma[3],aaW$arma[7],aaW$arma[4])
if(sum(aaW$arma[1:2])==0)
{orderWA[1]<-1}else
{NULL}
if(arima.force.seasonality=="y")
{if(sum(aaW$arma[3:4])==0)
{orderWS[1]<-1}else
{NULL}}else
{NULL}
Arimab<-Arima(dataa,order=orderWA,seasonal=list(order=orderWS),method="ML")
fArimab<-forecast(Arimab,h=8,simulate=TRUE,fan=TRUE) #returns the forecasts for the Arima model. h=number of periods for forecasting
if(aslog=="y")
{fArimabF<-exp(fArimab$mean[1:horiz])}else
{fArimabF<-fArimab$mean[1:horiz]} #if data was logged then its converted back by using the exponetial function as only interested in original data not transformed data
plot(fArimab,main="ARIMA Forecast",sub="blue=fitted,red=actual") #plots the forecast
lines(dataa,col="red",lwd=2) #changes colour and size of dataa
lines(ts(append(fitted(Arimab),fArimab$mean[1]),frequency=freqdata,start=startdata),col="blue",lwd=2) #shows the fitted arima on the same graph
if(aslog=="y")
{Arimab2f<-exp(fArimab$mean[1:horiz])}else
{Arimab2f<-fArimab$mean[1:horiz]} #if data was logged then its converted back by using the exponetial function as only interested in original data not transformed data
start(fArimab$mean)->startARIMA
ArimaALTf<-ts(prettyNum(Arimab2f,big.interval=3L,big.mark=","),frequency=freqdata,start=startARIMA) #puts forecasts in table
View(ArimaALTf,title="ARIMA2 final forecast") #brings up table of the forecasts
summary(Arimab)
######Percentage growth for Arima######
###when using this feature you will need to change lines 118-132, 137 to suit your data###
lastyr<-tail(datan,horiz) #selects the last values from the original data
percentf<-((Arimab2f/lastyr)-1)*100 #finds the percentage growth of the forecasted data
percentfr<-round(percentf,digits=2) #rounds the results to 3d.p
percentf_percent<-paste(percentfr,"%",sep="")
plot.ts(percentf,xaxt="n",ylab="Percentage growth rate",xlab="Quarter",main="Percentage Growth of the Forecasts") #plots the percentage growth of the forecasts and removes the x-axis values
axis(1,at=seq(1,horiz,by=1),las=1) #adds the choosen values onto the x-axis at the points
points(percentf,col="blue") #puts a blue circle around each of the points
for(i in 1:length(percentfr))
{text(x=i,y=percentfr[i],labels=percentf_percent[i],cex=0.5,pos=1)
text(x=i,y=percentfr[i],labels=percentf_percent[i],cex=0.5,pos=3)} #adds the percentage values next to the points on both the left and right side
fulldata<-c(datan,Arimab2f,0,0) #this makes a vector of all the data with the forecasts, the added zeros make it easier to seperate the years if it doesnt finish in the 4th quarter
fullmat<-matrix(fulldata,nrow=freqdata) #produces a matrix of the full data with the years seperated into columns
full1mat<-fullmat[,-1] #removes the first column from the matrix
full2mat<-matrix(c(full1mat,0,0,0,0),nrow=freqdata) #makes a matrix with a zero column at the end to account for the one removed in the last line
percent1<-((full2mat[,1]/fullmat[,1])-1)*100
percent2<-((full2mat[,2]/fullmat[,2])-1)*100
percent3<-((full2mat[,3]/fullmat[,3])-1)*100
percent4<-((full2mat[,4]/fullmat[,4])-1)*100
percent5<-((full2mat[,5]/fullmat[,5])-1)*100
percent6<-((full2mat[,6]/fullmat[,6])-1)*100
percent7<-((full2mat[,7]/fullmat[,7])-1)*100
percent8<-((full2mat[,8]/fullmat[,8])-1)*100
#percent9<-((full2mat[,9]/fullmat[,9])-1)*100 #add as many percents as there is years in the data
#percent10<-((full2mat[,10]/fullmat[,10])-1)*100
#percent11<-((full2mat[,11]/fullmat[,11])-1)*100
#percent12<-((full2mat[,12]/fullmat[,12])-1)*100
#percent13<-((full2mat[,13]/fullmat[,13])-1)*100
#percent14<-((full2mat[,14]/fullmat[,14])-1)*100
percentagegrowth<-c(percent1,percent2,percent3,percent4,percent5,percent6,percent7,percent8)#,percent9,percent10,percent11,percent12,percent13,percent14) #puts the percentage growths for each year in the same vector
percentagegrowth1<-head(percentagegrowth,-(length(fullmat)-length(datan))) #removes the unnecessary values from the end of the matrix
zero<-matrix(,nrow=(length(percentagegrowth1)-length(percentf))) #creates a matrix with no values
percentf1<-c(zero,percentf) #creates a vector with the NA values and the percantage growth of the forecast data
percentagegrowth1r<-round(percentagegrowth1,1)
names(percentagegrowth1)<-c("09 q1","09 q2","09 q3","09 q4","10 q1","10 q2","10 q3","10 q4","11 q1","11 q2","11 q3","11 q4","12 q1","12 q2","12 q3","12 q4","13 q1","13 q2","13 q3","13 q4","14 q1","14 q2","14 q3","14 q4","15 q1","15 q2")
percentagegrowth1_percent<-paste(percentagegrowth1r,"%",sep="")
plot.ts(percentagegrowth1,xaxt="n",xlab="Quarter",ylab="Percentage growth(%)",main="Year-over-Year Quarterly Percentage Growth") #plots all the percentage growth points
for(i in 1:length(percentagegrowth1r))
{text(x=i,y=percentagegrowth1r[i],labels=percentagegrowth1_percent[i],cex=0.5,font=2,pos=1)
text(x=i,y=percentagegrowth1r[i],labels=percentagegrowth1_percent[i],cex=0.5,font=2,pos=3)}
lines(percentf1,col="red") #adds the forecasted data as a red line
points(percentagegrowth1r,col="blue") #circles each of the points making them easier to see
axis(1,at=seq(1,length(percentagegrowth1),by=1),labels=names(percentagegrowth1),las=2,cex.axis=0.6)
legend("topright",c("Red=Forecasted data"))
