1 / 16

Seasonal Analysis

Seasonal Analysis. What is the period?. R-code. par(bg='yellow') plot(co2,lwd=2,col=4,xlab='Time, years',ylab='CO2 in parts per million (ppm)', main='Mauna Loa Atmospheric CO2 Concentration') abline(v=seq(1950,2006,by=10),lty=2) abline(h=seq(320,360,by=10),lty=2). R-code.

hhuynh
Download Presentation

Seasonal Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Seasonal Analysis

  2. What is the period? R-code par(bg='yellow') plot(co2,lwd=2,col=4,xlab='Time, years',ylab='CO2 in parts per million (ppm)', main='Mauna Loa Atmospheric CO2 Concentration') abline(v=seq(1950,2006,by=10),lty=2) abline(h=seq(320,360,by=10),lty=2)

  3. R-code # This line adds yearly grid abline(v=seq(1950,2006,by=1),lty=2)

  4. R-code # Linear trend time<-time(co2) l<-lsfit(time,co2) points(time,time*l$coefficients[2]+l$coefficients[1],type='l',col='green')

  5. R-code # Filter with 1-year window and plot a trend f<-filter(co2,rep(1,12)/12) points(f,col=2,type='l',lwd=2)

  6. R-code # Plot series minus trend plot(co2-f,ldw=2,col=4,lwd=2, xlab='Time, years',ylab='CO2-trend',main='Detrended CO2 series') abline(h=seq(-4,4,by=2),lty=2) abline(v=seq(1950,2006,by=1),lty=2)

  7. R-code # Perform seasonal analysis and plot season component m<-decompose(co2) plot(m$figure,lwd=2,type='p',pch=19,col=4, xlab='Month',ylab='Seasonal component') axis(1,at=seq(1,12)) grid(col='black',lty=2) UNR * STAT 758 * Fall 2006

  8. R-code m<-decompose(co2) plot(m)

  9. Trend Estimation by filtering

  10. Estimated signal

  11. Differencing

More Related