The R Project SVN R

Rev

Rev 20445 | Show entire file | Ignore whitespace | Details | Blame | Last modification | View Log | RSS feed

Rev 20445 Rev 30453
Line 1... Line 1...
1 
library(ts)
 1 
library(ts)
2 
.proctime00 <- proc.time()
 2 
.proctime00 <- proc.time()
3 
 
 3 
 
4 
### ar
 4 
### ar
5 
data(lh)
 - 
 
6 
ar(lh)
 5 
ar(lh)
7 
ar(lh, method = "burg")
 6 
ar(lh, method = "burg")
8 
ar(lh, method = "ols")
 7 
ar(lh, method = "ols")
9 
ar(lh, FALSE, 4) # fit ar(4)
 8 
ar(lh, FALSE, 4) # fit ar(4)
10 
ar.ols(lh)
 9 
ar.ols(lh)
11 
ar.ols(lh, FALSE, 4) # fit ar(4) by OLS
 10 
ar.ols(lh, FALSE, 4) # fit ar(4) by OLS
12 
 
 11 
 
13 
data(LakeHuron)
 - 
 
14 
ar(LakeHuron)
 12 
ar(LakeHuron)
15 
ar(LakeHuron, method = "burg")
 13 
ar(LakeHuron, method = "burg")
16 
ar(LakeHuron, method = "ols")
 14 
ar(LakeHuron, method = "ols")
17 
ar(LakeHuron, method = "mle")
 15 
ar(LakeHuron, method = "mle")
18 
 
 16 
 
19 
data(sunspot)
 - 
 
20 
ar(sunspot.year, method = "yw")
 17 
ar(sunspot.year, method = "yw")
21 
ar(sunspot.year, method = "burg")
 18 
ar(sunspot.year, method = "burg")
22 
ar(sunspot.year, method = "ols")
 19 
ar(sunspot.year, method = "ols")
23 
ar(sunspot.year, method = "mle")
 20 
ar(sunspot.year, method = "mle")
24 
 
 21 
 
25 
 
 22 
 
26 
### tests using presidents
 - 
 
27 
data(presidents) # contains missing values
 23 
### tests using presidents, contains missing values
28 
acf(presidents, na.action = na.pass)
 24 
acf(presidents, na.action = na.pass)
29 
pacf(presidents, na.action = na.pass)
 25 
pacf(presidents, na.action = na.pass)
30 
## graphs in example(acf) suggest order 1 or 3
 26 
## graphs in example(acf) suggest order 1 or 3
31 
(fit1 <- arima(presidents, c(1, 0, 0)))
 27 
(fit1 <- arima(presidents, c(1, 0, 0)))
32 
tsdiag(fit1)
 28 
tsdiag(fit1)
33 
(fit3 <- arima(presidents, c(3, 0, 0)))  # smaller AIC
 29 
(fit3 <- arima(presidents, c(3, 0, 0)))  # smaller AIC
34 
tsdiag(fit3)
 30 
tsdiag(fit3)
35 
 
 31 
 
36 
 
 32 
 
37 
### tests of arima:
 33 
### tests of arima:
38 
data(lh); data(USAccDeaths)
 - 
 
39 
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)))
 34 
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)))
40 
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)),
 35 
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order=c(0,1,1)),
41 
      method = "CSS") # drops first 13 observations.
 36 
      method = "CSS") # drops first 13 observations.
42 
 
 37 
 
43 
## test fitting with NAs
 38 
## test fitting with NAs
Line 68... Line 63...
68 
arima(x = LakeHuron, order = c(2, 0, 0), xreg = trend,
 63 
arima(x = LakeHuron, order = c(2, 0, 0), xreg = trend,
69 
      fixed = c(NA, NA, 580, 0))
 64 
      fixed = c(NA, NA, 580, 0))
70 
 
 65 
 
71 
 
 66 
 
72 
### model selection from WWWusage
 67 
### model selection from WWWusage
73 
data(WWWusage)
 - 
 
74 
aics <- matrix(, 6, 6, dimnames=list(p=0:5, q=0:5))
 68 
aics <- matrix(, 6, 6, dimnames=list(p=0:5, q=0:5))
75 
for(q in 1:5) aics[1, 1+q] <- arima(WWWusage, c(0,1,q),
 69 
for(q in 1:5) aics[1, 1+q] <- arima(WWWusage, c(0,1,q),
76 
    optim.control = list(maxit = 500))$aic
 70 
    optim.control = list(maxit = 500))$aic
77 
for(p in 1:5)
 71 
for(p in 1:5)
78 
   for(q in 0:5) aics[1+p, 1+q] <- arima(WWWusage, c(p,1,q),
 72 
   for(q in 0:5) aics[1+p, 1+q] <- arima(WWWusage, c(p,1,q),
Line 80... Line 74...
80 
round(aics - min(aics, na.rm=TRUE), 2)
 74 
round(aics - min(aics, na.rm=TRUE), 2)
81 
 
 75 
 
82 
 
 76 
 
83 
 
 77 
 
84 
### nottem
 78 
### nottem
85 
data(nottem)
 - 
 
86 
nott <- window(nottem, end=c(1936,12))
 79 
nott <- window(nottem, end=c(1936,12))
87 
fit <- arima(nott,order=c(1,0,0), list(order=c(2,1,0), period=12))
 80 
fit <- arima(nott,order=c(1,0,0), list(order=c(2,1,0), period=12))
88 
nott.fore <- predict(fit, n.ahead=36)
 81 
nott.fore <- predict(fit, n.ahead=36)
89 
ts.plot(nott, nott.fore$pred, nott.fore$pred+2*nott.fore$se,
 82 
ts.plot(nott, nott.fore$pred, nott.fore$pred+2*nott.fore$se,
90 
        nott.fore$pred-2*nott.fore$se, gpars=list(col=c(1,1,4,4)))
 83 
        nott.fore$pred-2*nott.fore$se, gpars=list(col=c(1,1,4,4)))
91 
 
 84 
 
92 
### StructTS
 85 
### StructTS
93 
data(UKgas)
 - 
 
94 
(fit <- StructTS(log10(UKgas), type = "BSM"))
 86 
(fit <- StructTS(log10(UKgas), type = "BSM"))
95 
(fit <- StructTS(log10(UKgas), type = "BSM", fixed=c(0, NA, NA, NA)))
 87 
(fit <- StructTS(log10(UKgas), type = "BSM", fixed=c(0, NA, NA, NA)))
96 
(fit <- StructTS(log10(UKgas), type = "BSM", fixed=c(NA, 0, NA, NA)))
 88 
(fit <- StructTS(log10(UKgas), type = "BSM", fixed=c(NA, 0, NA, NA)))
97 
(fit <- StructTS(log10(UKgas), type = "BSM", fixed=c(NA, NA, NA, 0)))
 89 
(fit <- StructTS(log10(UKgas), type = "BSM", fixed=c(NA, NA, NA, 0)))
98 
 
 90 
 
99 
### from AirPassengers
 91 
### from AirPassengers
100 
data(AirPassengers)
 - 
 
101 
## The classic `airline model', by full ML
 92 
## The classic `airline model', by full ML
102 
(fit <- arima(log10(AirPassengers), c(0, 1, 1),
 93 
(fit <- arima(log10(AirPassengers), c(0, 1, 1),
103 
              seasonal = list(order=c(0, 1 ,1), period=12)))
 94 
              seasonal = list(order=c(0, 1 ,1), period=12)))
104 
update(fit, method = "CSS")
 95 
update(fit, method = "CSS")
105 
update(fit, x=window(log10(AirPassengers), start = 1954))
 96 
update(fit, x=window(log10(AirPassengers), start = 1954))