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78
100.1
113.2
93.1
115.4
103.3
45.1
104.7
111.3
111.5
100.9
82.1
85.4
97.7
106.6
92.6
109.2
110
52.5
105.3
102.3
118.5
100
74.4
89.2
91.9
107
103.6
101.8
105.1
55.5
92.1
109.8
112.7
98.5
70.3
84.5
91.1
107.6
102.2
96
107.3
59.9
90.2
116.3
115.6
92
76.5
87.9
95.8
116.9
102.9
95.8
117.3
52.8
100.1
116.3
111.8
98.5
86.2
79.9
92.3
100.5
112.5
101.1
121.5
49.6
104.8
120.4
108.3
105.2
85.7
86.8
95.1
117
100.1
112.3
119.6
51.8
105.5
119.9
115.4
112.8
85.1
96.2
103.6
119.9
103.7
109
119.6
57
109.2
112.6
126
109.7
80.1
105.8
114.1
98.3
125.3
111.6
119.7
65
99
124.5
119
98.8
81.8
90.3
102
119.3
104.3
102.8
118.8
60.9
101
122.6
122.2
95
75.6
83.1
89.8
126.1
108.6
98.9
124.3
56.8
102.7
121.7
118.2
101
69
88.6
109.6
128.2
102
122.7
110.5
54
108.1
125
114.1
112.4
87.3
95.4
96.9
125.8
102
112.5
118.9
62.7
110
114.7
124.4
111.9
77
84.1
96.5
106.8
107.9
107.5
114.3
66.6
97.9
117.8
123.8
103.3
84.2
103.6
103.6
112.2
102.7
100.8
109.4
63.5
92.3
119.2
121.5
97.6
78.3
95.6
97.9
114.4
100.9
94.4
117.2
61
95.8
116.2
118.5
94.3
74.4
94.9
102
102.9
109.5
99.7
118.3
56.2
100.3
116.9
108.7
93.9
85.3
85.3
102.4
121.6
91.4
110.2
112.7
55.7
100.1

par8 <- ''
par7 <- '0.95'
par6 <- 'White Noise'
par5 <- '12'
par4 <- '1'
par3 <- '0'
par2 <- '1'
par1 <- '12'
if (par1 == 'Default') {
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
par7 <- as.numeric(par7)
if (par8 != '') par8 <- as.numeric(par8)
x <- na.omit(x)
ox <- x
if (par8 == '') {
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
} else {
x <- log(x,base=par8)
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='picts.png')
op <- par(mfrow=c(2,1))
plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')
if (par8=='') {
mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
} else {
mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')
mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
}
plot(x,type='l', main=mytitle,xlab='time',ylab='value')
par(op)
dev.off()
bitmap(file='pic1.png')
racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,'ACF(k)',header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 2:(par1+1)) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,'PACF(k)',header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')