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timeseries.R
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187 lines (118 loc) · 5.78 KB
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kings <- scan("http://robjhyndman.com/tsdldata/misc/kings.dat",skip=3)
kings
kingstimeseries <- ts(kings)
kingstimeseries
births <- scan("http://robjhyndman.com/tsdldata/data/nybirths.dat")
birthstimeseries <- ts(births, frequency=12, start=c(1946,1))
birthstimeseries
souvenir <- scan("http://robjhyndman.com/tsdldata/data/fancy.dat")
souvenirtimeseries <- ts(souvenir, frequency=12, start=c(1987,1))
souvenirtimeseries
plot.ts(kingstimeseries)
plot.ts(birthstimeseries)
plot.ts(souvenirtimeseries)
library("TTR")
kingstimeseriesSMA3 <- SMA(kingstimeseries,n=3)
plot.ts(kingstimeseriesSMA3)
kingstimeseriesSMA8 <- SMA(kingstimeseries,n=8)
plot.ts(kingstimeseriesSMA8)
birthstimeseriescomponents <- decompose(birthstimeseries)
birthstimeseriescomponents$seasonal
plot(birthstimeseriescomponents)
birthstimeseriescomponents <- decompose(birthstimeseries)
birthstimeseriesseasonallyadjusted <- birthstimeseries - birthstimeseriescomponents$seasonal
plot(birthstimeseriesseasonallyadjusted)
rain <- scan("http://robjhyndman.com/tsdldata/hurst/precip1.dat",skip=1)
rainseries <- ts(rain,start=c(1813))
plot.ts(rainseries)
rainseriesforecasts <- HoltWinters(rainseries, beta=FALSE, gamma= FALSE)
rainseriesforecasts
rainseriesforecasts$fitted
plot(rainseriesforecasts)
rainseriesforecasts$SSE
HoltWinters(rainseries, beta=FALSE, gamma=FALSE, l.start=23.56)
library("forecast")
rainseriesforecasts2 <- forecast.HoltWinters(rainseriesforecasts, h=8)
rainseriesforecasts2
plot.forecast(rainseriesforecasts2)
acf(rainseriesforecasts2$residuals, lag.max=20)
Box.test(rainseriesforecasts2$residuals, lag=20, type="Ljung-Box")
plot.ts(rainseriesforecasts2$residuals)
plotForecastErrors <- function(forecasterrors)
{
# make a red histogram of the forecast errors:
mybinsize <- IQR(forecasterrors)/4
mysd <- sd(forecasterrors)
mymin <- min(forecasterrors) + mysd*5
mymax <- max(forecasterrors) + mysd*3
mybins <- seq(mymin, mymax, mybinsize)
hist(forecasterrors, col="red", freq=FALSE, breaks=mybins)
# freq=FALSE ensures the area under the histogram = 1
# generate normally distributed data with mean 0 and standard deviation mysd
mynorm <- rnorm(10000, mean=0, sd=mysd)
myhist <- hist(mynorm, plot=FALSE, breaks=mybins)
# plot the normal curve as a blue line on top of the histogram of forecast errors:
points(myhist$mids, myhist$density, type="l", col="blue", lwd=2)
}
plotForecastErrors(rainseriesforecasts2$residuals)
skirts <- scan("http://robjhyndman.com/tsdldata/roberts/skirts.dat",skip=5)
skirtsseries <- ts(skirts,start=c(1866))
plot.ts(skirtsseries)
skirtsseriesforecasts <- HoltWinters(skirtsseries, gamma=FALSE)
skirtsseriesforecasts
plot(skirtsseriesforecasts)
HoltWinters(skirtsseries, gamma=FALSE, l.start=608, b.start=9)
skirtsseriesforecasts2 <- forecast.HoltWinters(skirtsseriesforecasts, h=19)
plot.forecast(skirtsseriesforecasts2)
acf(skirtsseriesforecasts2$residuals, lag.max=20)
Box.test(skirtsseriesforecasts2$residuals, lag=20, type="Ljung-Box")
plot.ts(skirtsseriesforecasts2$residuals)
plotForecastErrors(skirtsseriesforecasts2$residuals)
logsouvenirtimeseries <- log(souvenirtimeseries)
souvenirtimeseriesforecasts <- HoltWinters(logsouvenirtimeseries)
souvenirtimeseriesforecasts
souvenirtimeseriesforecasts$SSE
plot(souvenirtimeseriesforecasts)
souvenirtimeseriesforecasts2 <- forecast.HoltWinters(souvenirtimeseriesforecasts, h=48)
plot.forecast(souvenirtimeseriesforecasts2)
acf(souvenirtimeseriesforecasts2$residuals, lag.max=20)
Box.test(souvenirtimeseriesforecasts2$residuals, lag=20, type="Ljung-Box")
plot.ts(souvenirtimeseriesforecasts2$residuals) # make a time plot
plotForecastErrors(souvenirtimeseriesforecasts2$residuals) # make a histogram
skirtsseriesdiff1 <- diff(skirtsseries, differences=1)
plot.ts(skirtsseriesdiff1)
skirtsseriesdiff2 <- diff(skirtsseries, differences=2)
plot.ts(skirtsseriesdiff2)
kingtimeseriesdiff1 <- diff(kingstimeseries, differences=1)
plot.ts(kingtimeseriesdiff1)
acf(kingtimeseriesdiff1, lag.max=20)
acf(kingtimeseriesdiff1, lag.max=20, plot= FALSE)# get the autocorrelation values
pacf(kingtimeseriesdiff1, lag.max=20) # plot a partial correlogram
pacf(kingtimeseriesdiff1, lag.max=20, plot=FALSE) # get the partial autocorrelation values
volcanodust <- scan("http://robjhyndman.com/tsdldata/annual/dvi.dat", skip=1)
volcanodustseries <- ts(volcanodust,start=c(1500))
plot.ts(volcanodustseries)
acf(volcanodustseries, lag.max=20) # plot a correlogram
acf(volcanodustseries, lag.max=20, plot= FALSE) # get the values of the autocorrelations
pacf(volcanodustseries, lag.max=20)
pacf(volcanodustseries, lag.max=20, plot=FALSE)
kingstimeseriesarima <- arima(kingstimeseries, order=c(0,1,1)) # fit an ARIMA(0,1,1) model
kingstimeseriesarima
library("forecast") # load the "forecast" R library
kingstimeseriesforecasts <- forecast.Arima(kingstimeseriesarima, h=5)
kingstimeseriesforecasts
plot.forecast(kingstimeseriesforecasts)
acf(kingstimeseriesforecasts$residuals, lag.max=20)
Box.test(kingstimeseriesforecasts$residuals, lag=20, type="Ljung-Box")
plot.ts(kingstimeseriesforecasts$residuals) # make time plot of forecast errors
plotForecastErrors(kingstimeseriesforecasts$residuals) # make a histogram
volcanodustseriesarima <- arima(volcanodustseries, order=c(2 ,0,0))
volcanodustseriesarima
volcanodustseriesforecasts <- forecast.Arima(volcanodustseriesarima, h=31)
volcanodustseriesforecasts
plot.forecast(volcanodustseriesforecasts)
acf(volcanodustseriesforecasts$residuals, lag.max=20)
Box.test(volcanodustseriesforecasts$residuals, lag=20, typ e="Ljung-Box")
plot.ts(volcanodustseriesforecasts$residuals) # make time plot of forecast errors
plotForecastErrors(volcanodustseriesforecasts$residuals) # make a histogram
mean(volcanodustseriesforecasts$residuals)