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stlplot.R
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stlplot.R
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library(forecast)
library(fpp)
stlplot <- function(choice){
#reading in data
#please replace the above address with yours
##########################################################################################
Stock = read.table(paste("/home/ubuntu/ShinyApps/spredict/stocks/",choice,".csv",sep = ""),sep = ",",header = TRUE)
#converting to month is not needed when you download monthly data
#Stock$Date = as.Date(Stock$Date)
#Stock_monthly = agregate(Stock$close,by = list(Date=format(Stock$Date,"%Y%m")),mean)
#converting to ts
tryCatch({ tsStock = ts(Stock$Close,start=c(2008,1),end=c(2015,6),frequency=12)})
max_value = max(tsStock)
min_value = min(tsStock)
#Generalize function as Polynomial "trend" (TREND = toStocktrend1)
t1 = seq(2008,2015,length=length(tsStock))
t12 = t1^7
polyStock = lm(tsStock ~ t1 + t12)
tsStocktrend1 = ts(polyStock$fit,start=c(2008,1),frequency=12)
#plot(tsStock,lw=2,col="blue",xlim=c(2000,2013))
#lines(tsStocktrend1,lw=2,col="red")
#this abline function plots lines in the currently plotted graph
#the v argument states where to draw the dotted vertical line
#lty is line type, here lty=3 means a dotted line
#abline(v=2015.5,lty=3)
#Decompose a time series into seasonal, trend and irregular components based on loess method
# get second generalized "trend" function (TREND = tsStocktrend2)
stlStock = stl(tsStock,s.window = "periodic")
#plot(stlStock,col="blue",lw=2)
tsStocktrend2 = stlStock$time.series[,2]
#plot(forecast(stlStock))
#abline(v=2015.5,lty = 3)
#plot(tsStock,lw=3)
#lines(tsStocktrend1,col="purple",lw=2)
#lines(tsStocktrend2,col="red",lw=2)
#abline(v=2015.5,lty=3)
#legend("bottomleft",legend=c("Actual Function","STL trend","Polynomial Trend"),col=c("black","red","purple"),lw=2)
#start predicting #
#based on polynomial function#
HWStock1_ng = HoltWinters(tsStocktrend1,gamma=FALSE)
HWStock1 = HoltWinters(tsStocktrend1)
NETfit1 <- nnetar(tsStocktrend1)
autofit1 = auto.arima(tsStocktrend1)
#fit12 <- arima(tsStocktrend1,order=c(1,0,0),seasonal = list(order=c(2,1,0),period=12),method = "ML",optim.method="Nelder-Mead")
fit11 <- tslm(tsStocktrend1 ~ trend + season, lambda=0)
stlStock1 = stl(tsStocktrend1,s.window="periodic")
#Based on STL function #
HWStock2_ng = HoltWinters(tsStocktrend2,gamma=FALSE)
HWStock2 = HoltWinters(tsStocktrend2)
NETfit2 <- nnetar(tsStocktrend2)
autofit2 = auto.arima(tsStocktrend2)
#fit2 <- Arima(tsStocktrend2, order=c(15,3,3))
#fit22 <- arima(tsStocktrend2, order=c(1,0,0),list(order=c(2,1,0),period=12))
fit12 <- tslm(tsStocktrend2 ~ trend +season, lambda = 0)
stlStock2 = stl(tsStocktrend1,s.window="periodic")
tobereturned <- plot(forecast(autofit1,h=24),xlim=c(2008,2017.2),ylim=c(min_value-30,max_value+50),lw=2,col="red",xlab="Time",ylab="Stock Price",main="Predictions of the STL trend")
lines(tsStock,lw=3)
lines(forecast(stlStock2,h=24)$mean,col="red",lw=2)
#lines(forecast(fit2,h=24)$mean,lw=2,col="purple")
lines(tsStocktrend2,lw=2,col="red")
lines(forecast(NETfit2,h=24)$mean,lw=3,lty="longdash",col="brown")
lines(predict(HWStock2,n.ahead=24),lw=2,col="green")
lines(predict(HWStock2_ng,n.ahead=24),lw=2,col="green")
lines(predict(HWStock2,n.ahead=24,prediction.interval=T,level=0.95)[,2],col="orange")
lines(predict(HWStock2,n.ahead=24,prediction.interval=T,level=0.95)[,3],col="orange")
legend("bottomleft",legend=c("Actual Function","STL Trend","Predicion - Holt Winters","Prediction - Arima(auto)","Prediciton - Neural Nets","Prediction - Linear Model"),col=c("black","red","green","blue","purple","brown","orange"),lw=2,cex=0.75)
abline(v=2015.5,lty=3)
return(tobereturned)
}