#EXTRACTING TREND, SEASONAL, ERROR, MAUNA LOA DATA www = "https://www.mimuw.edu.pl/~noble/courses/TimeSeries/data/atmospheric-carbon-dioxide-recor.csv" carbon = read.csv(www) carbon = carbon[-611,] y = carbon$MaunaLoaCO2 MaunLoaCo2 = ts(data = y, frequency = 12) output.stl = stl(MaunLoaCo2, s.window = "periodic") plot(output.stl) a <- output.stl$time.series acf(a) #GET STANDARD DEVIATIONS FOR THE SEASONAL, TREND AND ERROR apply(a,2,sd) #HOLT WINTERS FILTERING ON AIR PASSENGER DATA data(AirPassengers) AP <- AirPassengers str(AP) ?HoltWinters AP.hw <- HoltWinters(AP,seasonal="mult") plot(AP.hw) legend("topleft",c("observed","fitted"),lty=1,col=1:2) AP.predict <-predict(AP.hw,n.ahead=4*12) ts.plot(AP,AP.predict,lty=1:2)