An Example.

The question

Data

Analyses

Conclusions

Monthly mean air temperature at Recife 1953-1962

The question:

What is the relationship between temperatures in Recife and El Nino?

Objectives

- to layout analyses

- to explore the data for surprises

- predicted values

- signal + noise?

- ...

Finding the data.

Google with various key words: temperature, Recife, ...

"Eventually lead" to:

cdiac.ornl.gov/ftp/ndp041

Carbon dioxide information analysis center!

Had to discover Recife Curado station id - 3068290000

(Looked at Brasil sites too, but that didn't turn up the data)

Years 1949-1988

Searched an inappropriate ste for a long time

30682900001941 274 279 268 267 260 250 246 245 256 260 262 266

30682900001942 273 268 270 270 256 247 236 233 252 260 270 265

30682900001943 270 270 273 261 253 245 236 238 247 260 264 268

30682900001944 269 272 275 263 254 247 239 232 241 256 267 273

30682900001945 278 268 278 271 256 240 236 243 251 258 268 267

30682900001946 268 277 271 259 258 247 247 247 249 257 261 266

30682900001947 269 270 268-9999 253 246 244 245 249 262 268-9999

30682900001948 273 271 270 269 255 249 243 240 248-9999 264 270

30682900001949 272 274 278 266 252 246 240 236 250 261 265 271

30682900001950 273 280 269 251 248 243 235 234 248 258 264-9999

30682900001951 269 267 275 265 254 238 233 238 247 259 263-9999

30682900001952 272 278 267 262 252 245 240 238 253 256 263 268

The web data. monthly

notice -9999 replace by NA file: recifecurado

How to handle missing values? Interpolate? Model? ...?

junk<-scan("recifecurado")

junk1<-matrix(junk,ncol=48)

junk2<-junk1[2:13,] # years in first row

series<-c(junk2)/10 # for degrees centigrade

length(series[is.na(series)]) #17 - need to understand missingness

Interpolation

series1<-series

for(i in 2:(length(series)-1)){if(is.na(series[i]))series1[i]<-.5*series[i-1] +.5*series[i+1]}

plot(xaxis,series1,type="l",xlab="year",ylab="mean temp (degrees C)",las=1)

title("Mean monthly temperatures 1949-88 Recife Curado")

abline(h=mean(series1))

There is seasonality and variability

Restricted range in mid-sixties - nonconstant mean level?

ylim<-range(series1)

par(mfrow=c(2,1))

plot(lowess(xaxis,series1),type="l",ylim=ylim,xlab="year",ylab="degrees C",main="Smoothed Recife series")

abline(h=mean(series1))

junk20<-lowess(xaxis,series1)

plot(xaxis,series1-junk20$y,type="l",xlab="year",ylab="degrees C",main="Residuals")

abline(h=mean(series1-junk20$y))

par(mfrow=c(1,1))

acf(series1,las=1,xlab="lag(mo)",ylab="",main="autocorrelation recife temperatures",lag.max=50,ylim=c(-1,1))

More confirmation of period 12

Note that nearby values are highly correlated

REMEMBER the interpretation of the error lines

spectrum(series1,xlab="frequency (cycles/month)",las=1)

Note peaks at frequency 1/12 and harmonics

Further confirmation of period 12

Note log scale for y-axis

Note vertical line in upper right

Gives uncertainty

What is the shape of the seasonal?

junk4<-matrix(series1,nrow=12)

junk5<-apply(junk4,1,mean)

plot(junk5,type="l",las=1)

abline(h=mean(junk5))

Cooler in July-Aug

Southern Hemisphere

Uncertainty?

Cooler in July-August. Southern hemisphere

Part of a longer cycle? El Nino explanatory?

After "removing" trend middle has been pulled up

Need uncertainties

Back to original data

Remove seasonal

series2<-series1

for(i in 1:48){

for(j in 1:12){

series2[(i-1)*12+j]<-series1[(i-1)*12+j]-junk5[j]

}

}

par(mfrow=c(2,1))

plot(xaxis,series2,type="l",xlab="year",ylab="residual",main="Series after removing seasonal",las=1)

abline(h=0)

ylim<-range(series2)

plot(xaxis,series1-mean(series1),type="l",xlab="year",ylab="degreesC",main="Mean removed series",las=1,ylim=ylim)

abline(h=mean(series1-mean(series1)))

original variance 1.342 adjusted .248

par(mfrow=c(2,1))

acf(series2,lag.max=50,las=1,xlab="lag (mo)",main="Ajusted by removing monthly means",las=1)

acf(diff(series1,lag=12),lag.max=50,xlab="lag (mo)",main="Order 12 differenced series")

Frequency domain analysis.

par(mfrow=c(2,1))

junk9<-spec.pgram(series1,taper=0,detrend=F,demean=F,spans=5,plot=F)

ylim<-range(junk9$spec)

junk9<-spec.pgram(series1,taper=0,detrend=F,demean=F,spans=5,xlab="frequency (cycles/mo)",las=1,main="Original series")

junk10<-spec.pgram(series2,taper=0,detrend=F,demean=F,spans=5,ylim=ylim,main="Monthly means removed",las=1)

Work remains on seasonal

Residual "not" white noise

Time domain

distributions

Parametric model. SARIMA ?

Thinking about prediction, consider

Yt = αYt-1 + βYt-12 + Nt

with some ARMA for Nt

Check seasonal residuals for normality

Hope to end up with white noise

Call:

arima(x = series1, order = c(1, 0, 1), seasonal = list(order = c(1, 0, 1), period = 12))

Coefficients:

ar1 ma1 sar1 sma1 intercept

0.7604 -0.3344 0.9990 -0.9201 25.6117

s.e. 0.0610 0.0968 0.0007 0.0220 0.6100

sigma^2 estimated as 0.1771: log likelihood = -337.48, aic = 686.97

Junk<-arima(series1,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12))

tsdiag(Junk,gof.lag=25)

Junk<-arima(series1,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12))

postscript(file="recifeplots1a.ps",paper="letter",hor=T)

Junk2<-predict(Junk,n.ahead=24)

Junk3<-c(series1,Junk2$pred)

Junk3a<-c(rep(0,576),2*Junk2$se)

Junk3b<-c(rep(0,576),-2*Junk2$se)

Junk4a<-Junk3+Junk3a;Junk4b<-Junk3+Junk3b

ylim<-range(Junk4a,Junk4b)

par(mfrow=c(1,1))

xaxis1<-1941+(1:length(Junk3)/12)

plot(xaxis1[xaxis1>1983],Junk4a[xaxis1>1983],type="l",las=1,ylim=ylim,col="red",xlab="year",ylab="degrees C",main="Data + predictions")

lines(xaxis1[xaxis1>1983],Junk4b[xaxis1>1983],col="red")

lines(xaxis1[xaxis1>1983],Junk3[xaxis1>1983],col="blue")

lines(xaxis[xaxis>1983],series1[xaxis>1983])

Two series

Bivariate case {Xt, Yt} - jointly distributed

Linear time invariant / transfer function model

tkktkt NXhY

nonparametric/parametric approaches

Southern Oscillation Index

El Niño: global coupled ocean-atmosphere phenomenon.

The Pacific ocean signatures, El Niño and La Niña are important temperature fluctuations in surface waters of the tropical Eastern Pacific Ocean

Southern Oscillation reflects monthly or seasonal fluctuations in the air pressure difference between Tahiti and Darwin

www.cpc.ncep.noaa.gov/data/soi

junk<-scan("recifecurado")

junk1<-matrix(junk,ncol=48)

junk6<-junk1[1,]

junk1<-junk1[,junk6>1950]

junk2<-junk1[2:13,]

series<-c(junk2)/10

length(series[is.na(series)]) #13

xaxis<-1951+(1:length(series)/12)

series1<-series

junk4<-matrix(series1,nrow=12)

junk5<-apply(junk4,1,mean)

for(i in 2:(length(series)-1)){if(is.na(series[i]))series1[i]<-.5*series[i-1]+.5*series[i+1]}

junk4<-matrix(series1,nrow=12)

junk5<-apply(junk4,1,mean)

series2<-series1

for(i in 1:38){

for(j in 1:12){

series2[(i-1)*12+j]<-series1[(i-1)*12+j]-junk5[j]}}

kunk<-scan("SOIa.dat")

kunk1<-matrix(kunk,ncol=58); kunk6<-kunk1[1,]

kunk1<-kunk1[,kunk6<1989]

kunk2<-kunk1[2:13,]

teries<-c(kunk2)

length(teries[is.na(teries)]) #0

teries1<-teries; teries2<-teries1

postscript(file="recifeplots3.ps",paper="letter",hor=T)

par(mfrow=c(2,1))

plot(xaxis,series2,type="l",las=1,xlab="year",ylab="",main="Seasonally adjusted Recife temps")

plot(xaxis,teries2,type="l",las=1,xlab="year",ylab="",main="Southern Oscillation Index")

postscript(file="recifeplots2.ps",paper="letter",hor=F)

par(mfrow=c(1,1))

acf(cbind(series2,teries2))

junk10<-cbind(series2,teries2)

junk11<-spec.pgram(junk10,plot=F,taper=0,detrend=F,demean=F,spans=11)

par(mfcol=c(2,2))

plot(junk11$freq,10**(.1*junk11$spec[,2]),log="y",main="SOIspectrum", xlab="frequency", ylab="", las=1,type="l")

plot(junk11$freq,junk11$coh,main="Coherence",xlab="frequency",ylab="",las=1,ylim=c(0,1),type="l")

junkh<-1-(1-.95)**(1/(.5*junk11$df-1))

abline(h=junkh)

plot(junk11$freq,10**(.1*junk11$spec[,1]),log="y",main="Seasonally corrected Recife spectrum",xlab="frequency", ylab="",las=1, type="l")

SARIMAX

Yt = αYt-1 + βYt-12 + γXt + Nt

ar1 ma1 sar1 sma1 intercept teries1

0.7885 -0.3717 0.9996 -0.9474 25.5572 -0.0255

s.e. 0.0610 0.1014 0.0006 0.0321 0.6403 0.0149

sigma^2 estimated as 0.1792: log likelihood = -275.68, aic = 565.35

Junk1<-arima(series1,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12),xreg=teries1)

The answer to the question:

There is a hint of a linear time invariant relationship.