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findBestPrediction.r
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findBestPrediction.r
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# Adding missing libraries e.g for forecast: install.packages("forecast")
library(forecast)
library(fpp)
tryCatch({ findBestPrediction <- function(Stockadd)
{
Stock = Stockadd
# In case not read correctly:
backUpStock = read.table("./input/AEE.csv", sep=",", header=TRUE);
# Convert into time series object
tryCatch({ tsStock = ts(rev(Stock$Close),start=c(2000, 1),frequency=12)}, error=function(e) { tsStock = ts(rev(Stock$Close),start=c(2006, 1),frequency=12) })
tryCatch({ tsBackUpStock = ts(rev(backUpStock$Close),start=c(2000, 1),frequency=12)}, error=function(e) { backUpStock = ts(rev(Stock$Close),start=c(2006, 1),frequency=12) })
# Create Train and Test data of the input stock
tryCatch({ train <- window(tsBackUpStock, end=2010)}, error=function(e) { train = 0 })
tryCatch({ test <- window(tsBackUpStock, start=2013)}, error=function(e) { test = 0 })
tryCatch({ Btrain <- window(tsBackUpStock, end=2010)}, error=function(e) { Btrain = 0 })
tryCatch({ Btest <- window(tsBackUpStock, start=2013)}, error=function(e) { Btest = 0 })
tryCatch({ train <- window(tsStock, end=2010)}, error=function(e) { train = 0 })
tryCatch({ test <- window(tsStock, start=2013)}, error=function(e) { test = 0 })
if (test[1] == Btest[1] && test[2] == Btest[2] && test[3] == Btest[3]) {train = Btrain }
# Mean Absolute Errors of the 25 predictions are stored here
tryCatch({ mae = matrix(NA,25,length(test)+1)}, error=function(e) { mae = matrix(NA,25,10000) })
tl = seq(2000,2013,length=length(train))
tl2 = tl^7
# cat("01")
tryCatch({ polyStock = lm(train ~ tl + tl2)}, error=function(e) { polyStock = 0 })
# cat("02")
tryCatch({ tsStocktrend1=ts(polyStock$fit,start=c(2000, 1),frequency=12)}, error=function(e) { tsStocktrend1 = 0 })
# cat("03")
tryCatch({ stlStock = stl(train,s.window="periodic")}, error=function(e) { stlStock = 0 })
# cat("04")
tryCatch({ tsStocktrend2 = stlStock$time.series[,2]}, error=function(e) { tsStocktrend2 = 0 })
# cat("05")
tryCatch({ HWStock1_ng = HoltWinters(tsStocktrend1,gamma=FALSE)}, error=function(e) { HWStock1_ng = 0 })
# cat("06")
tryCatch({ HWStock1 = HoltWinters(tsStocktrend1)}, error=function(e) { HWStock1 = 0 })
# cat("07")
tryCatch({ NETfit1 <- nnetar(tsStocktrend1)}, error=function(e) { NETfit1 = 0 })
# cat("08")
# tryCatch({ autofit1 = auto.arima(tsStocktrend1)}, error=function(e) { autofit1 = 0 })
# cat("09")
tryCatch({ fit12 <- arima(tsStocktrend1, order=c(1,0,0), list(order=c(2,1,0), period=12))}, error=function(e) { fit12 = 0 })
# cat("010")
tryCatch({ fitl1 <- tslm(tsStocktrend1 ~ trend + season, lambda=0)}, error=function(e) { fitl1 = 0 })
# cat("011")
tryCatch({ stlStock1 = stl(tsStocktrend1,s.window="periodic")}, error=function(e) { stlStock1 = 0 })
# cat("012")
tryCatch({ HWStock2_ng = HoltWinters(tsStocktrend2,gamma=FALSE)}, error=function(e) { HWStock2_ng = 0 })
# cat("013")
tryCatch({ HWStock2 = HoltWinters(tsStocktrend2)}, error=function(e) { HWStock2 = 0 })
# cat("014")
tryCatch({ NETfit2 <- nnetar(tsStocktrend2)}, error=function(e) { NETfit2 = 0 })
# cat("015")
# tryCatch({ autofit2 = auto.arima(tsStocktrend2) }, error=function(e) { autofit2 = 0 })
# cat("016")
tryCatch({ fit2 <- arima(tsStocktrend2, order=c(15,3,3))}, error=function(e) { fit2 = 0 })
# cat("017")
tryCatch({ fit22 <- arima(tsStocktrend2, order=c(1,0,0), list(order=c(2,1,0), period=12))}, error=function(e) { fit22 = 0 })
# cat("018")
tryCatch({ fitl2 <- tslm(tsStocktrend2 ~ trend + season, lambda=0)}, error=function(e) { fitl2 = 0 })
# cat("019")
tryCatch({ stlStock2 = stl(tsStocktrend1,s.window="periodic")}, error=function(e) { stlStock2 = 0 })
# cat("020")
tryCatch({ HWStockr_ng = HoltWinters(train,gamma=FALSE)}, error=function(e) { HWStockr_ng = 0 })
# cat("021")
tryCatch({ HWStockr = HoltWinters(train)}, error=function(e) { HWStockr = 0 })
# cat("022")
tryCatch({ NETfitr <- nnetar(train)}, error=function(e) { NETfitr = 0 })
# cat("023")
# tryCatch({ autofitr = auto.arima(train)}, error=function(e) { autofitr = 0 })
# cat("024")
tryCatch({ fitr <-arima(train, order=c(15,3,3))}, error=function(e) { fitr = 0 })
# cat("025")
tryCatch({ fitr2 <- arima(train, order=c(1,0,0), list(order=c(2,1,0), period=12))}, error=function(e) { fitr2 = 0 })
# cat("026")
tryCatch({ fitlr <- tslm(train ~ trend + season, lambda=0)}, error=function(e) { fitlr = 0 })
# cat("027")
tryCatch({ stlStockr = stl(train,s.window="periodic")} , error=function(e) {stlStockr = 0 })
# cat(" TRANSITION -!!!-")
# cat("1")
tryCatch({ HWStockr_ng = HoltWinters(train,gamma=FALSE)}, error=function(e) { HWStockr_ng = 0 })
# cat("2")
# tryCatch({ predautofitr = window(forecast(autofitr,h=39)$mean, start=2013)}, error=function(e) { predautofitr = 0 })
# cat("3")
tryCatch({ predfitr = window(forecast(fitr,h=39)$mean, start=2013)}, error=function(e) { predfitr = 0 })
# cat("4")
tryCatch({ predfitr2 = window(forecast(fitr2,h=39)$mean, start=2013)}, error=function(e) { predfitr2 = 0 })
# cat("5")
tryCatch({ predNETfitr = window(forecast(NETfitr,h=39)$mean, start=2013)}, error=function(e) { predNETfitr = 0 })
# cat("6")
tryCatch({ predHWStockr = window(predict(HWStockr,n.ahead=39), start=2013)}, error=function(e) { predHWStockr = 0 })
# cat("7")
tryCatch({ predHWStockr_ng = window(predict(HWStockr_ng,n.ahead=39), start=2013)}, error=function(e) { predHWStockr_ng = 0 })
# cat("8")
# tryCatch({ predautofit2 = window(forecast(autofit2,h=39)$mean, start=2013)}, error=function(e) { predautofit2 =0 })
# cat("9")
tryCatch({ predfit12 = window(forecast(fit12,h=39)$mean, start=2013)}, error=function(e) { predfit12 = 0 })
# cat("10")
tryCatch({ predfit2 = window(forecast(fit2,h=39)$mean, start=2013)}, error=function(e) { predfit2 = 0 })
# cat("11")
tryCatch({ predfit22 = window(forecast(fit22,h=39)$mean, start=2013)}, error=function(e) { predfit22 = 0 }) # cat("C2")}, error=function(e) { predfit22 = 0 })
# cat("12")
tryCatch({ predstlStock1 = window( forecast(stlStock1, h=39)$mean, start=2013)}, error=function(e) { predstlStock1 = 0 })
# cat("13")
tryCatch({ predstlStock2 = window(forecast(stlStock2, h=39)$mean, start=2013)}, error=function(e) { predstlStock2 = 0 })
# cat("14")
tryCatch({ predstlStockr = window(forecast(stlStockr, h=39)$mean, start=2013)}, error=function(e) { predstlStockr = 0 })
# cat("15")
tryCatch({ predNETfit2 = window(forecast(NETfit2,h=39)$mean, start=2013)}, error=function(e) { predNETfit2 = 0 })
tryCatch({ predHWStock2 = window(predict(HWStock2,n.ahead=39), start=2013)}, error=function(e) { predHWStock2 = 0 })
tryCatch({ predHWStock2_ng = window(predict(HWStock2_ng,n.ahead=39), start=2013)}, error=function(e) { predHWStock2_ng = 0 })
# tryCatch({ predautofit1 = window(forecast(autofit1,h=39)$mean, start=2013)}, error=function(e) { predautofit1 = 0 })
# cat("after autofit")
tryCatch({ predfitlr = window(forecast(fitlr, h=39)$mean , start=2013)}, error=function(e) { predfitlr = 0 })
tryCatch({ predfitl1 = window(forecast(fitl1, h=39)$mean, start=2013)}, error=function(e) { predfitl1 = 0 })
tryCatch({ predfitl2 = window(forecast(fitl2, h=39)$mean , start=2013)}, error=function(e) { predfitl2 = 0 })
tryCatch({ predNETfit1 = window(forecast(NETfit1,h=39)$mean, start=2013)}, error=function(e) { predNETfit1 = 0 })
tryCatch({ predHWStock1_ng = window(predict(HWStock1_ng,n.ahead=39), start=2013)}, error=function(e) { predHWStock1_ng = 0 })
tryCatch({ predHWStock11 = window(predict(HWStock1, n.ahead = 39, prediction.interval = T, level = 0.95)[,1], start=2013)}, error=function(e) { predHWStock11 = 0 })
tryCatch({ predHWStock12 = window(predict(HWStock1, n.ahead = 39, prediction.interval = T, level = 0.95)[,2], start=2013)}, error=function(e) { predHWStock12 = 0 })
tryCatch({ predHWStock13 = window(predict(HWStock1, n.ahead = 39, prediction.interval = T, level = 0.95)[,3], start=2013)}, error=function(e) { predHWStock13 = 0 })
# cat(" NEXT --!!!--")
# Calculate Mean Absolute Error
for(i in 1:length(test))
{
# mae[1,i] <- abs <- abs(predautofitr[i]-test[i])
tryCatch({ mae[2,i] <-abs(predfitr[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[3,i] <- abs <- abs(predfitr2[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[4,i] <- abs(predNETfitr[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[5,i] <- abs(predHWStockr[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[6,i] <- abs(predHWStockr_ng[i]-test[i]) }, error=function(e) { })
# mae[7,i] <- abs(predautofit2[i]-test[i])
tryCatch({ mae[8,i] <- abs(predfit12[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[9,i] <-abs(predfit2[i]-test[i]) }, error=function(e) { })
# cat("before mae 22")
tryCatch({ mae[10,i] <- abs(predfit22[i]-test[i]) }, error=function(e) { })
# cat("after mae 22")
tryCatch({ mae[11,i] <- abs(predstlStock1[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[12,i] <- abs(predstlStock2[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[13,i] <- abs(predstlStockr[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[14,i] <- abs(predNETfit2[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[15,i] <- abs(predHWStock2[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[16,i] <- abs(predHWStock2_ng[i]-test[i]) }, error=function(e) { })
# mae[17,i] <- abs(predautofit1[i]-test[i])
tryCatch({ mae[18,i] <- abs(predfitlr[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[19,i] <- abs(predfitl1[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[20,i] <- abs(predfitl2[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[21,i] <- abs(predHWStock1_ng[i]-test[i] ) }, error=function(e) { })
tryCatch({ mae[22,i] <- abs(predNETfit1[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[23,i] <- abs(predHWStock11[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[24,i] <- abs(predHWStock12[i]-test[i]) }, error=function(e) { })
tryCatch({ mae[25,i] <- abs(predHWStock13[i]-test[i]) }, error=function(e) { })
}
# Sum all Errors
for(i in 1:25)
{
mae[i,5] = sum(mae[i,1:4])
}
# Find best Prediction
best = which.min(mae[1:25,5])
cat(" - winning model ID:", best )
return (best)
}}, error=function(e) { cat("findBestPrediction failed for:",Stockadd); });