Examples of X-13ARIMA SEATS in R R-interface to X-13ARIMA-SEATS
Examples from the official manual
This page collects the examples from the official X-13ARIMA-SEATS manual in the R package seasonal. The models have been tested and run without additional data in R. Most of the models can be run online.
7.1 ARIMA
Example 1
series { title = "Quarterly Grape Harvest" start = 1950.1
period = 4
data = (8997 9401 ... 11346) }
arima { model = (0 1 1) }
estimate { }
R-code:
seas(AirPassengers,
x11 = "",
arima.model = "(0 1 1)"
)
seas(AirPassengers,
x11 = "",
arima.model = c(0, 1, 1)
)
Remark(s):
- If the
x11
spec is specified (here, as an empty spec), the defaultseats
spec is automatically disabled. - ARIMA models may be specified as a character string or as a numeric vector.
Example 2
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
transform { function = log } arima{model =(210)(011)} estimate { }
R-code:
seas(AirPassengers,
x11 = "",
transform.function = "log",
arima.model = "(2 1 0)(0 1 1)"
)
Example 3
Series { Title = "Monthly Sales" Start = 1976.jan
Data = (138 128 ... 297) }
Transform { Function = log }
Regression { Variables= (seasonal const) } Arima {Model=(011)}
Estimate { }
R-code:
seas(AirPassengers,
x11 = "",
transform.function = "log",
regression.variables = c("seasonal", "const"),
arima.model = "(0 1 1)"
)
Example 4
series{title = "Annual Olive Harvest" start = 1950
data = (251 271 ... 240) }
arima{model = ([2] 1 0)}
estimate{ }
R-code:
seas(AirPassengers,
x11 = "",
arima.model = "([2] 1 0)"
)
Example 5
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
transform { function = log }
regression { variables = const }
arima { model = (0 1 1)12 }
estimate { }
R-code:
seas(AirPassengers,
x11 = "",
transform.function = "log",
regression.variables = c("const"),
arima.model = "(0 1 1)12"
)
Example 6
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
transform { function = log }
regression { variables = (const seasonal)} arima{model =(110)(100)3(001)} estimate { }
R-code:
seas(AirPassengers,
x11 = "",
transform.function = "log",
regression.variables = c("const", "seasonal"),
arima.model = "(1 1 0)(1 0 0)3(0 0 1)"
)
Example 7
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
transform{ function = log }
arima { model = (0 1 1)(0 1 1)12
ma = ( ,1.0f)}
estimate { }
R-code:
seas(AirPassengers,
x11 = "",
transform.function = "log",
arima.model = "(0 1 1)(0 1 1)12",
arima.ma = " , 1.0f"
)
Remark(s):
- Because the first element in the
arima.ma
argument is empty, it has to be entered as a character string instead of a numeric vector.
7.2 AUTOMDL
Example 1
series { title = "Monthly sales" start = 1976.jan
file="ussales.dat" }
regression { variables = (td seasonal) } automdl { }
estimate { }
x11 {}
R-code:
seas(AirPassengers,
x11 = "",
regression.variables = c("td", "seasonal")
)
Remark(s):
- If the
x11
spec is specified (here, as an empty spec), the defaultseats
spec is automatically disabled.
Example 2
series { title = "Monthly sales"
file="ussales.dat" }
regression { variables = td }
start = 1976.jan
automdl {
diff=(11)
maxorder = ( 3, ) }
outlier { }
estimate { } x11 {}
R-code:
seas(AirPassengers,
x11 = "",
regression.variables = c("td"),
automdl.diff = c(1, 1),
automdl.maxorder = "3, "
)
Remark(s):
- Because the second element in the
automdl.maxorder
argument is empty, it has to be entered as a character string instead of a numeric vector.
Example 3
series { title = "Monthly sales"
file="ussales.dat" }
regression { aictest = td } automdl { savelog = amd } estimate { }
x11 {}
R-code:
m <- seas(AirPassengers,
x11 = "",
regression.aictest = c("td"),
automdl.savelog = "amd"
)
out(m)
Remark(s):
- With the HTML version, the
log
output can be analyzed in the browser with theout()
function. Click onLog Entry
in the sidebar.
7.3 CHECK
Example 1
series { title = "Monthly Retail Sales"
start = 1964.jan
file = "sales1.dat" }
regression { variables = (td ao1967.jun
ls1971.jun easter[14]) }
arima { model = (0 1 1)(0 1 1) }
check { print = (all) }
R-code:
m <- seas(AirPassengers,
x11 = "",
arima.model = "(0 1 1)(0 1 1)",
check.print = "all"
)
out(m)
Remark(s):
- With the HTML version, the output can be analyzed in the browser.
Example 2
series { title = "Monthly Retail Sales"
start = 1964.jan
file = "sales1.dat" }
regression { variables = (td ao1967.jun
ls1971.jun easter[14]) }
arima { model = (0 1 1)(0 1 1) }
check { print = (all -pacf -pacfplot)
maxlag = 36 }
R-code:
m <- seas(AirPassengers,
x11 = "",
regression.variables = c("td", "ao1951.jun", "ls1953.jun", "easter[14]"),
arima.model = c(0, 1, 1, 0, 1, 1),
check.print = c("all", "-pacf", "-pacfplot"),
check.maxlag = 36
)
out(m)
Remark(s):
- With the HTML version, the output can be analyzed in the browser.
7.4 COMPOSITE
Remark(s):
- The
composite
spec is not supported.
7.5 ESTIMATE
Example 1
series { title = "Monthly Sales" start = 1976.1
data = (138 128 ... 297) }
regression { variables = seasonal }
arima { model = (0,1,1) ma = (0.25f) }
estimate { save = residuals }
R-code:
m <- seas(AirPassengers,
x11 = "",
regression.variables = c("seasonal"),
arima.model = c(0, 1, 1),
arima.ma = "0.25f"
)
resid(m)
Remark(s):
- Residuals are imported by default and can be accessed with
resid
.
Example 2
series { title = "Monthly Inventory" start = 1978.12
data = (1209 834 ... 1002) }
transform { function = log }
regression { variables = (td ao1999.01) }
arima { model = (1,1,0)(0,1,1) }
estimate { tol = 1e-4 maxiter = 100 exact = ma save = mdl
print = (iterations roots) }
R-code:
m <- seas(AirPassengers,
x11 = "",
outlier = NULL,
transform.function = "log",
regression.variables = c("td", "ao1959.01"),
arima.model = c(1, 1, 0, 0, 1, 1),
regression.aictest = NULL,
estimate.tol = 1e-4,
estimate.maxiter = 100,
estimate.exact = "ma"
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 3
series { title = "Monthly Inventory" start = 1978.12
data = (1209 834 ... 1002) }
transform { function = log }
estimate { file = "Inven.mdl"
fix = all }
R-code:
seas(x = AirPassengers,
x11 = "",
regression.variables = c("td", "ao1959.01"),
estimate.maxiter = 100,
estimate.exact = "ma",
arima.model = "(1 1 0)(0 1 1)",
regression.aictest = NULL,
outlier = NULL,
transform.function = "log",
regression.b = c("-0.6f", "-0.3f", "-0.3f", "-0.3f", "0.3f", "0.4f", "0.4f"),
arima.ma = "0.57462f",
arima.ar = "-0.22557f"
)
Remark(s):
- Instead of saving the
.mdl
file as an input, users should directly save the call toseas
.
This can be done with the help of the static
function.
7.6 FORCE
Example 1
SERIES { TITLE="EXPORTS OF TRUCK PARTS" START =1967.1
FILE = "X21109.ORI" }
PICKMDL { }
X11 { SEASONALMA = S3X9 }
FORCE { START = OCTOBER }
R-code:
seas(AirPassengers,
pickmdl = "",
x11.seasonalma = "S3X9",
force.start = "oct"
)
Example 2
SERIES { TITLE="EXPORTS OF TRUCK PARTS" START =1967.1
FILE = "X21109.ORI" }
PICKMDL { }
X11 { SEASONALMA = S3X9 }
FORCE { START = OCTOBER
TYPE = REGRESS
RHO = 0.8
}
R-code:
seas(AirPassengers,
pickmdl = "",
x11.seasonalma = "S3X9",
force.start = "oct",
force.type = "regress",
force.rho = 0.8
)
Example 3
Series { Title="Imports Of Truck Engines" Start =1967.1
File = "I21110.Ori" }
Pickmdl { }
X11 { Seasonalma = S3X5 }
Force { Type = None }
R-code:
seas(AirPassengers,
pickmdl = "",
x11.seasonalma = "S3X5",
force.type = "none"
)
7.7 FORECAST
Example 1
SERIES { TITLE = "Monthly sales" START = 1976.JAN
DATA = (138 128 ... 297) }
TRANSFORM { FUNCTION = LOG } REGRESSION { VARIABLES = TD } ARIMA {
MODEL = (0 1 1)(0 1 1)12 } FORECAST { }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast = ""
)
series(m, "forecast.forecasts")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 2
Series { Title = "Monthly Sales" Start = 1976.jan
Data = (138 128 ... 297) }
Transform { Function = Log}
Regression { Variables = Td }
Arima { Model = (0 1 1)(0 1 1)12 }
Estimate { }
Outlier { }
Forecast { Maxlead = 24 }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast.maxlead = 24
)
series(m, "forecast.forecasts")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS. - The
estimate
andoutlier
spec are activated by default.
Example 3
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
transform { function = log }
regression { variables = td }
arima { model = (0 1 1)(0 1 1)12 }
estimate { }
forecast { maxlead = 15
probability = .90
exclude = 10 }
R-code:
m <- seas(AirPassengers,
x11 = "",
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast.maxlead = 15,
forecast.probability = 0.9,
forecast.exclude = 10
)
series(m, "forecast.forecasts")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 4
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297)
span = ( ,1990.mar) }
transform { function = log}
regression { variables = td }
arima { model = (0 1 1)(0 1 1)12 }
estimate { }
forecast { maxlead = 24 }
R-code:
seas(window(AirPassengers, end = c(1958, 3)),
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast.maxlead = 24
)
seas(AirPassengers,
series.span = " ,1958.mar",
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast.maxlead = 24
)
Remark(s):
- Shortening the span in R or X-13 is equivalent.
Example 5
series { title = "monthly sales" start = 2000.jan
file = "ussales.dat" }
transform { function = log }
regression { variables = td }
arima { model = (0 1 1)(0 1 1)12 }
forecast { maxback=12 }
x11{ }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast.maxback = 12,
x11 = ""
)
series(m, "forecast.forecasts")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 6
Series { Title = "Monthly Sales" Start = 1976.jan
Data = (138 128 ... 297) }
Transform { Function = Log}
Regression { Variables = Td }
Arima { Model = (0 1 1)(0 1 1)12 }
Estimate { }
Outlier { }
Forecast { Maxlead = 24 Lognormal = Yes }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
regression.variables = "td",
arima.model = "(0 1 1)(0 1 1)12",
forecast.maxlead = 24,
forecast.lognormal = "yes"
)
series(m, "forecast.forecasts")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
7.8 HISTORY
Example 1
Series { Title = "Sales Of Livestock" Start = 1967.1
File = "cattle.ori" }
X11 { SeasonalMA = S3X9 }
History { sadjlags = 2 }
R-code:
m <- seas(austres,
x11.seasonalma = "S3X9",
history.sadjlags = 2
)
series(m, "history.sarevisions")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 2
series { title = "Exports of Leather goods"
start = 1969.jul file = "expleth.dat" }
regression { variables = (const td ls1972.may ls1976.oct) } arima { model=(012)(110) }
estimate { }
history { estimates = fcst fstep = 1 start=1975.jan }
R-code:
m <- seas(AirPassengers,
regression.variables = c("const", "td", "ls1952.may", "ls1956.oct"),
arima.model= "(0 1 2)(1 1 0)",
x11.seasonalma = "S3X9",
history.estimates = "fcst",
history.fstep = 1,
history.start = "1955.jan"
)
series(m, "fch")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 3
series { title = "Exports of Leather goods"
start = 1969.jul
file = "expleth.dat" }
regression { variables = (const td ls1972.may ls1976.oct) } arima { model=(012)(110) }
estimate { }
history { estimates = fcst save = r6 start = 1975.jan }
R-code:
m <- seas(AirPassengers,
regression.variables = c("const", "td", "ls1952.may", "ls1956.oct"),
arima.model= "(0 1 2)(1 1 0)",
history.estimates = "fcst",
history.save = "fch"
)
series(m, "fch")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 4
series { title = "Housing Starts in the Midwest"
start = 1967.1
file = "hsmwtot.ori"
modelspan = (,0.Dec)
comptype=add
}
regression { variables = td } arima{model=(012)(011) }
x11 { seasonalMA = S3X3 }
history { estimates = (sadj trend) }
R-code:
m <- seas(AirPassengers,
regression.variables = "td",
arima.model= "(0 1 2)(0 1 1)",
x11.seasonalma = "S3X3",
history.estimates = c("sadj", "trend")
)
series(m, "sar")
series(m, "trr")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 5
composite{ title = "Total Housing Starts in the US"
modelspan = (,0.Dec)
}
regression { variables = td } arima{model=(011)(011) } x11 { seasonalMA = S3X3 }
history { estimates = (sadj trend)
save = (sar iar trr) }
R-code:
m <- seas(AirPassengers,
regression.variables = "td",
arima.model= "(0 1 1)(0 1 1)",
x11.seasonalma = "S3X3",
history.estimates = c("sadj", "trend")
)
series(m, "sar")
series(m, "trr")
Remark(s:
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS. - The
composite
spec is not supported.
7.9 METADATA
Remark(s):
- Output and diagnostics are handeled by the seasonal package. No need to use this spec.
7.10 IDENTIFY
Example 1
series { title = "Monthly Sales" start = 1976.jan
data = (138 128 ... 297) }
transform { function = log }
identify { diff = (0, 1)
sdiff = (0, 1)
print = (none +acf) }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
identify.diff = c(0, 1),
identify.sdiff = c(0, 1),
identify.print = c("none", "+acf")
)
out(m)
Remark(s):
- With the HTML version, the output can be analyzed in the browser.
Example 2
SERIES { TITLE = "MONTHLY SALES" START = 1976.JAN
DATA = (138 128 ... 297) }
REGRESSION { VARIABLES = (CONST SEASONAL) }
IDENTIFY { DIFF = (0,1) }
R-code:
m <- seas(AirPassengers,
regression.variables = c("const", "seasonal"),
identify.diff = c(0,1)
)
Example 3
Series { Title = "Monthly Sales" Start = 1976.Jan
Data = (138 128 ... 297) }
Transform { Function = Log }
Regression { Variables = (Td Easter[14])}
Identify { Diff = (1) Sdiff = (1) Maxlag = 30
Print = (None +ACFplot +PACFplot) }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
regression.variables = c("td", "easter[14]"),
identify.diff = 1,
identify.sdiff = 1,
identify.maxlag = 30,
identify.print = c("none", "+acfplot", "+pacfplot")
)
out(m)
Remark(s):
- With the HTML version, the output can be analyzed in the browser.
Example 4
series { title = "Quarterly Sales" start = 1963.1 period = 4
data = (56.7 57.7 ... 68.0) }
regression { variables = (ls1971.1) }
arima { model = (0 1 1)(0 1 1) }
identify { diff = (0, 1) sdiff = (0, 1) maxlag = 16 }
estimate { }
check { }
R-code:
seas(AirPassengers,
regression.variables = c("ls1952.1"),
identify.diff = c(0, 1),
identify.sdiff = c(0, 1),
identify.maxlag = 16
)
7.11 OUTLIER
Example 1
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
arima { model = (0 1 1)(0 1 1)12 }
outlier { lsrun = 5 types=(ao ls) }
R-code:
seas(AirPassengers,
arima.model= "(0 1 1)(0 1 1)12",
outlier.lsrun = 5,
outlier.types = c("ao", "ls")
)
Example 2
Series { Title = "Monthly Sales" Start = 1976.Jan
Data = (138 128 ... 297)
Span = (1980.Jan, 1992.Dec) }
Regression { Variables = (LS1981.Jun LS1990.Nov) }
Arima { Model = (0 1 1)(0 1 1)12 }
Estimate { }
Outlier { Types = AO Method = Addall Critical = 4.0 }
R-code:
seas(window(AirPassengers, start = c(1950, 1), end = c(1959, 12)),
regression.variables = c("ls1951.jun", "ls1952.nov"),
arima.model= "(0 1 1)(0 1 1)12",
outlier.lsrun = 5,
outlier.types = "ao",
outlier.method = "addall",
outlier.critical = 4
)
Example 3
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297)
span = (1980.jan, 1992.dec) } arima{ model=(011)(011)12}
estimate { }
outlier { types = ls
critical = 3.0
lsrun = 2
span = (1987.jan, 1988.dec) }
R-code:
seas(window(AirPassengers, start = c(1950, 1), end = c(1959, 12)),
outlier.types = "ls",
outlier.critical = 3,
outlier.lsrun = 2,
outlier.span = "1953.jan, 1958.dec"
)
Example 4
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297)
span = (1980.jan, 1992.dec) } arima{model =(011)(011)12}
estimate { }
outlier { critical = (3.0, 4.5, 4.0)
types = all }
R-code:
seas(window(AirPassengers, start = c(1950, 1), end = c(1959, 12)),
arima.model= "(0 1 1)(0 1 1)12",
outlier.critical = c(3, 4.5, 4),
outlier.types = "all"
)
7.12 PICKMDL
Example 1
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
regression { variables = (td seasonal) }
pickmdl { mode = fcst file = "nosdiff.mdl" } estimate { }
x11 {}
R-code:
seas(AirPassengers,
x11 = "",
pickmdl.mode = "fcst"
)
Remark(s):
- If the
x11
spec is specified (here, as an empty spec), the defaultseats
spec is automatically disabled. - If the
pickmdl
spec is specified, the defaultautomdl
spec is automatically disabled.
Example 2
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
regression { variables = td }
pickmdl { mode = fcst file = "nosdiff.mdl"
method = first fcstlim = 20 qlim = 10
overdiff = 0.99 identify = all }
outlier { }
estimate { }
x11 {}
R-code:
seas(AirPassengers,
x11 = "",
regression.variables = "td",
pickmdl.mode = "fcst",
pickmdl.method = "first",
pickmdl.fcstlim = 20,
pickmdl.qlim = 10,
pickmdl.overdiff = 0.99,
pickmdl.identify = "all"
)
Example 3
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
regression { variables = td }
pickmdl { mode = fcst file = "nosdiff.mdl"
outofsample=yes }
estimate { }
x11 {}
R-code:
seas(AirPassengers,
x11 = "",
regression.variables = "td",
pickmdl.mode = "fcst",
pickmdl.outofsample = "yes"
)
7.13 REGRESSION
Example 1
SERIES { TITLE = "Monthly sales" START = 1976.JAN
DATA = (138 128 ... 297) }
REGRESSION { VARIABLES = (CONST SEASONAL) }
ARIMA { MODEL = (0 1 1) }
ESTIMATE { }
R-code:
seas(AirPassengers,
regression.aictest = NULL,
regression.variables = c("const", "seasonal"),
arima.model = "(0 1 1)"
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 2
series { title = "Irregular Component of Monthly Sales"
start = 1976.jan
file = "sales.d13"
format = "x13save"
}
regression { variables = (const sincos[4,5]) }
estimate { }
spectrum { savelog=peaks }
R-code:
m <- seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
regression.variables = c("const", "sincos[4,5]"),
spectrum.savelog = "peaks"
)
out(m)
Remark(s):
- With the HTML version, the
log
output can be analyzed in the browser with theout()
function. Click onLog Entry
in the sidebar. regression.aictest
has to be turned off for fully manual variable specification.
Example 3
Series { Title = "Monthly Sales" Start = 1976.Jan
Data = (138 128 ... 297) }
Transform { Function = Log }
Regression { Variables = (TD Easter[8] Labor[10] Thank[3]) }
Identify { Diff = (0 1) SDiff = (0 1) }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.aictest = NULL,
regression.variables = c("const", "easter[8]", "thank[3]"),
identify.diff = c(0, 1),
identify.sdiff = c(0, 1)
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 4
series { title = "Monthly sales" start = 1976.jan
data = (138 128 ... 297) }
transform { function = log }
regression { variables = (tdnolpyear lom easter[8] labor[10] thank[3])
aictest = (lom td easter) }
arima { model = (0 1 1)(0 1 1) }
estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.aictest = NULL,
regression.variables = c("tdnolpyear", "lom", "easter[8]", "labor[10]",
"thank[3]"),
arima.model = "(0 1 1)(0 1 1)"
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 5
series { title = "Retail inventory of food products"
start = 1990.jan data = "foodri.dat" type = stock
}
regression { variables = ( tdstock1coef[31] easterstock[8] )
aictest = ( td easter )
}
arima { model=(011)(011) } x11{ }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("tdstock1coef[31]", "easterstock[8]"),
arima.model = "(0 1 1)(0 1 1)",
x11 = ""
)
Example 6
Series { Title = "Quarterly Sales" Start = 1990.1 Period = 4
Data = (1039 1241 ... 2210) }
Transform { Function = Log }
Regression { Variables = (AO2007.1 RP2005.2-2005.4 AO1998.1 TD) }
Arima { Model=(011)(011)}
Estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.aictest = NULL,
regression.variables = c("ao1950.1", "rp1950.2-1950.4", "ao1951.1", "td"),
arima.model = "(0 1 1)(0 1 1)"
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 7
Series { Title = "Quarterly Sales" Start = 1990.1 Period = 4
Data = (1039 1241 ... 2210) }
Transform { Function = Log }
Regression { Variables = (AO2007.1 QI2005.2-2005.4 AO1998.1 TD) }
Arima { Model=(011)(011)}
Estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.aictest = NULL,
regression.variables = c("ao1950.1", "qi1950.2-1950.4", "ao1951.1", "td"),
arima.model = "(0 1 1)(0 1 1)"
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 8
series {title = "Quarterly sales" start = 1981.1
data = (301 294 ... 391) period = 4 }
regression {user = tls
data = (0 0 0 0 0 0 0 0 0 0 0 0 ...
0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 ... 0) }
identify { diff = (0 1) sdiff = (0 1) }
R-code:
# user defined regressor
tls <- ts(0, start = 1949, end = 1965, freq = 12)
window(tls, start = c(1955, 1), end = c(1957, 12)) <- 1
seas(AirPassengers,
xreg = tls,
identify.diff = c(0, 1),
identify.sdiff = c(0, 1),
outlier = NULL
)
Example 9
series {title = "Quarterly sales" start = 1981.1
data = (301 294 ... 391) period = 4 }
regression { variables = tl1985.03-1987.01 }
identify { diff = (0 1) sdiff = (0 1) }
R-code:
seas(AirPassengers,
regression.variables = c("tl1955.01-1957.12"),
identify.diff = c(0, 1),
identify.sdiff = c(0, 1),
outlier = NULL)
Remark(s):
outlier
has to be turned off for manual outlier specification.
Example 10
series { title = "Monthly Riverflow" start = 1970.1
data = (8.234 8.209 ... 8.104) period = 12 }
regression { variables = (seasonal const)
user = (temp precip)
file = "weather.dat"
format = "(t17,2f8.2)"
start = 1960.1 }
arima { model = (3 0 0)(0 0 0) }
estimate { }
R-code:
temp = ts(runif(200), start = 1948, frequency = 12)
precip = ts(runif(200), start = 1948, frequency = 12)
seas(AirPassengers,
x11 = "",
xreg = cbind(temp, precip),
regression.variables = c("seasonal", "const"),
arima.model = "(3 0 0)(0 0 0)",
regression.aictest = NULL
)
Remark(s):
- use R time series as external variables.
regression.aictest
has to be turned off for fully manual variable specification.
Example 11
series {title = "Retail Inventory - Family Apparel"
start = 1967.1 period = 12
data = (1893 1932 ... 3201 )
type = stock }
transform { function = log }
regression { variables = (tdstock[31] ao1980.jul)
aictest=tdstock }
arima { model = (0 1 0)(0 1 1) }
estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("tdstock[31]", "ao1950.jul"),
arima.model = "(0 1 0)(0 1 1)",
regression.aictest = "tdstock"
)
Example 12
series { title = "Retail Sales - Televisions"
start = 1976.1 period = 12 type = flow
file = ’tvsales.ori’ }
transform { function = log }
regression { variables = (td/1985.dec/ seasonal/1985.dec/) }
arima { model = (0 1 1) }
estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("td/1952.dec/", "seasonal/1952.dec/"),
arima.model = "(0 1 1)",
x11 = ""
)
Example 13
series {title = "Retail Sales - Televisions"
start = 1976.1 period = 12 type = flow
file = ’tvsales.ori’ }
transform { function = log }
regression { variables = (td td//1985.dec/
seasonal seasonal//1985.dec/) } arima{ model=(011)}
estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("td", "td//1952.dec/", "seasonal",
"seasonal//1952.dec/"),
arima.model = "(0 1 1)",
x11 = ""
)
Example 14
Series { Title = "Quarterly Sales" Start = 1993.1 Period = 4
Data = (1039 1241 ... 2210) }
Transform { Function = Log }
Regression { Variables = (AO2001.3 LS2007.1 LS2007.3 AO2008.4) }
Arima { Model=(011)(011)}
Estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("ao1950.1", "ls1952.2", "ls1952.3", "ao1951.1"),
arima.model = "(0 1 1)(0 1 1)"
)
Example 15
Series { Title = "Quarterly Sales" Start = 1993.1 Period = 4
Data = (1039 1241 ... 2210) }
Transform { Function = Log }
Regression { Variables = (AO2001.3 TL2007.1-2007.2 AO2008.4) }
Arima { Model=(011)(011)}
Estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("ao1950.1", "tl1952.2-1952.3", "ao1951.1"),
arima.model = "(0 1 1)(0 1 1)"
)
Example 16
Series { Title = "Quarterly Sales" Start = 1993.1 Period = 4
Data = (1039 1241 ... 2210) }
Transform { Function = Log }
Regression { Variables = (AO2001.3 LSS2007.1-2007.3 AO2008.4) }
Arima { Model=(011)(011)}
Estimate { }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("ao1950.1", "ls1952.2-1952.3", "ao1951.1"),
arima.model = "(0 1 1)(0 1 1)"
)
Example 17
series { title = "Exports of pasta products"
start = 1980.jan data = "pasta.dat" }
regression { variables = (const td) }
automdl { }
x11 { mode = add }
R-code:
seas(AirPassengers,
transform.function = "none",
regression.variables = c("const", "td"),
x11.mode = "add"
)
Example 18
series{ title = "Retail sales of children’s apparel"
file = "capprl.dat" start = 1975.1 }
transform{ function = log }
regression{
variables = (const td ao1976.oct ls1991.dec easter[8] seasonal)
user = (sale88 sale89 sale90)
start = 1975.1 file = "promo.dat" format = "(3f12.0)" }
arima{ model = (2 1 0) }
forecast{ maxlead = 24 }
x11{ save=seasonal appendfcst=yes }
R-code:
ser1 = ts(runif(200), start = 1948, frequency = 12)
ser2 = ts(runif(200), start = 1948, frequency = 12)
ser3 = ts(runif(200), start = 1948, frequency = 12)
seas(AirPassengers,
transform.function = "none",
xreg = cbind(ser1, ser2, ser3),
regression.variables = c("const", "td", "ao1956.oct", "ls1951.dec",
"easter[8]", "seasonal"),
arima.model = c(2, 1, 0),
x11.appendfcst = "yes"
)
Example 19
series{ title = "Retail sales of children’s apparel"
file = "capprl.dat" start = 1975.1 }
transform{ function = log }
regression{
variables = (const td ao1976.oct ls1991.dec easter[8]
seasonal)
user = (sale88 sale89 sale90)
start = 1975.1 file = "promo.dat" format = "(3f12.0)"
usertype = ao
}
arima{ model = (2 1 0) }
forecast{ maxlead = 24 }
x11{ save=seasonal appendfcst=yes }
R-code:
ser1 = ts(runif(200), start = 1948, frequency = 12)
ser2 = ts(runif(200), start = 1948, frequency = 12)
ser3 = ts(runif(200), start = 1948, frequency = 12)
seas(AirPassengers,
transform.function = "none",
xreg = cbind(ser1, ser2, ser3),
regression.usertype = "ao",
regression.variables = c("const", "td", "ao1956.oct", "ls1951.dec",
"easter[8]", "seasonal"),
arima.model = c(2, 1, 0),
x11.appendfcst = "yes"
)
Example 20
series{
format = "2L"
title = "Midwest Total Starts"
file = "mwtoths.dat"
name = "MWTOT "
}
transform{ function=log }
arima{ model=(012)(011) }
estimate{ save=mdl }
regression{
variables = (ao1977.jan ls1979.jan ls1979.mar ls1980.jan td)
b = ( -0.7946F -0.8739F 0.6773F -0.6850F 0.0209
~0.0107 -0.0022 0.0018 ~0.0088 -0.0074 )
}
x11{ }
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("ao1957.jan", "ls1959.jan", "ls1959.mar",
"ls1960.jan", "td"),
regression.b = c("-0.7946f", "-0.8739f", "0.6773f", "-0.6850f",
"0.0209", "-0.0107", "-0.0022", "0.0018", "-0.0088",
"-0.0074"),
regression.aictest = NULL,
arima.model = "(0 1 2)(0 1 1)",
x11 = ""
)
Remark(s):
- use
static(m, coef = TRUE)
to extract a call with fixed coefficients. regression.aictest
has to be turned off for fully manual variable specification.
Example 21
Series {
Format="1L" File="bdptrs.dat" Name="BDPTRS"
Title="Department Store Sales" }
Transform { Function=Log }
Regression { Variables=( Td Easter[8] )
Save = ( Td Holiday ) }
Arima { Model=(0 1 1)(0 1 1) }
Example 21
Outlier {
Estimate {
Check {
Forecast { }
X11 {Mode = Mult Seasonalma = S3X3
Title = ("Department Store Retail Sales Adjusted For")
"Outlier, Trading Day, And Holiday Effects" )
}
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("td", "easter[8]"),
regression.aictest = NULL,
arima.model = "(0 1 1)(0 1 1)",
x11.mode = "mult",
x11.seasonalma = "S3X3"
)
Remark(s):
regression.aictest
has to be turned off for fully manual variable specification.
Example 22
series{ title = "US Total Housing Starts"
file = "ustoths.dat" start = 1990.1
period = 4 save = b1}
transform{ function = log }
regression{
user = (s1 s2 s3)
usertype = seasonal
start = 1985.1 file = "seasreg.rmx"
format = "x13save"
}
outlier{ }
arima{ model = (0 1 1) }
forecast{ maxlead = 24 }
R-code:
ser1 = ts(runif(200), start = 1948, frequency = 12)
ser2 = ts(runif(200), start = 1948, frequency = 12)
ser3 = ts(runif(200), start = 1948, frequency = 12)
seas(AirPassengers,
x11 = "",
transform.function = "log",
xreg = cbind(ser1, ser2, ser3),
regression.usertype = "seasonal",
regression.aictest = NULL,
arima.model = c(0, 1, 1),
forecast.maxlead = 24
)
Remark(s):
- use R time series as external variables.
regression.aictest
has to be turned off for fully manual variable specification.
Example 23
series{
file="serv.dat" start=1991.jan span=(1993.jan,)
title = "Payment to family nanny, taiwan"
}
transform{ function=log }
regression{
variables = ( AO1995.Sep AO1997.Jan AO1997.Feb )
user=( Beforecny Betweencny Aftercny
Beforemoon Betweenmoon Aftermoon
Beforemidfall Betweenmidfall Aftermidfall )
file="u1u2u3.dat"
format="datevalue"
start=1991.1
Example 23
usertype=( holiday
holiday2 holiday2
holiday3 holiday3
chi2test = yes
savelog = chi2test
}
holiday
holiday2
holiday3 )
holiday
arima{ model=(0 1 1)(0 1 0)
check{ }
forecast{ maxlead=12 }
estimate{ savelog=(aic aicc bic) }
R-code:
# construct chinese new year time series with 'genhol' function
data(holiday)
cny1 <- genhol(cny, start = -6, end = -1, frequency = 12, center = "calendar")
cny2 <- genhol(cny, start = 0, end = 6, frequency = 12, center = "calendar")
seas(AirPassengers,
transform.function = "log",
xreg = cbind(cny1, cny2),
regression.usertype = c("holiday", "holiday2"),
regression.variables = c("AO1955.Sep", "AO1957.Jan", "AO1957.Feb"),
arima.model = "(0 1 1)(0 1 0)",
forecast.maxlead = 12,
x11 = ""
)
Remark(s):
- use
genhol
to construct holiday time series (see?genhol
).
7.14 SEATS
Example 1
SERIES { TITLE="EXPORTS OF TRUCK PARTS"
START =1987.1
FILE = "X21109.ORI"
PERIOD = 12
}
TRANSFORM { FUNCTION = AUTO }
REGRESSION { AICTEST = TD }
AUTOMDL { }
OUTLIER { TYPES = (AO LS TC) }
FORECAST { MAXLEAD = 36 }
SEATS { SAVE = S11 }
R-code:
seas(AirPassengers,
regression.aictest = "td",
outlier.types = c("ao", "ls", "tc"),
forecast.maxlead = 36
)
Remark(s):
transform.function = "auto"
, andautomdl = ""
andseats = ""
are activated by default.
Example 2
Series { Title="Quarterly Exports Of Mangos"
Start =1990.1 File = "Xmango.Ori" Period = 4 }
Transform { Function = Log } Regression { Aictest = Td } Arima{ Model=(011)(011) } Forecast { Maxlead = 12 }
Seats { Finite = yes
Save = ( Squaredgainsaconc Timeshiftsaconc )
Savelog = Overunderestimation
}
History { Estimates = (Sadj Trend)
Save = ( Sarevisions Trendrevisions )
}
R-code:
m <- seas(AirPassengers,
regression.aictest = "td",
arima.model = "(0 1 1)(0 1 1)",
forecast.maxlead = 12,
seats.finite = "yes",
history.estimates = c("sadj", "trend"),
history.save = c("sarevisions", "trendrevisions")
)
series(m, c("history.sarevisions", "history.trendrevisions"))
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 3
Series { Title="Model based adjustment of Bimonthly exports"
Start = 1995.1 File = "Xports6.Ori" Period = 6 }
Transform { Function = Log } Regression { Variables = Td }
Arima{ Model=(011)(011) } Outlier { types = (ao ls tc) }
Forecast { Maxlead = 18 }
Seats { save = (S11 S10 S12) }
R-code:
# bimonthly data
require(tempdisagg)
AirPassengersBM <- ta(AirPassengers, to = 6)
m <- seas(AirPassengersBM,
regression.aictest = NULL,
outlier.types = c("ao", "ls", "tc"),
forecast.maxlead = 18
)
final(m)
Remark(s):
- X-13ARIMA-SEATS needs monthly or quarterly data for trading day.
regression.aictest
has to be turned off for fully manual variable specification.
7.15 SERIES
Example 1
series{
title = "A Simple Example"
start = 1967.jan # period defaults to 12
data=(480 467 514 505 534 546 539 541 551 537 584
854 522 506 558 538 605 583 607 624 570 609 675 861 .
.
.
1684 1582 1512 1508 1574 2303 1425 1386) }
R-code:
seas(AirPassengers)
Remark(s):
- Time series specification is handled by
seasonal
. No manual input needed.
Example 2
series { data = (879 899 985 ...) # There are 216 data values
start = 1940.1 # ending in 1993.4
period = 4 # Quarterly series
span = (1946.1, 1990.4) }
R-code:
seas(window(AirPassengers, start = c(1950, 1), end = c(1959, 12)))
seas(AirPassengers, series.span = "1950.1, 1959.12")
Remark(s):
- Shortening the span in R or X-13 is equivalent.
Example 3
SERIES{ TITLE = "Monthly data in an X-11 format"
PERIOD = 12
FILE = "C:\DATA\SALES1.DAT" # a DOS path and file
PRECISION = 1
FORMAT = "1r" }
R-code:
seas(AirPassengers)
Remark(s):
- Time series specification is handled by
seasonal
. No manual input needed.
Example 4
series {title = "Data read correctly in with trimzero = no"
start = 1980.2 period = 12
file = "example4.new" } # file is in current directory
R-code:
seas(AirPassengers)
Remark(s):
- Time series specification is handled by
seasonal
. No manual input needed.
Example 5
SERIES{ TITLE = "Monthly data in a datevalue format"
PERIOD = 12
FILE = "C:\DATA\SALES1.EDT" # a DOS path and file
FORMAT = "DATEVALUE" TYPE = FLOW }
R-code:
seas(AirPassengers,
series.type = "flow"
)
Example 6
SERIES{ TITLE = "Monthly data in a datevalue format"
PERIOD = 12
Example 6
Example 7
This example shows how the X-13ARIMA-SEATS program handles missing data. The same data format is used as in the previous two examples, except a missing value code is inserted for January of 1990:
FILE = "C:\DATA\SALES1.EDT"
FORMAT = "DATEVALUE"
COMPTYPE = ADD
DECIMALS = 2
MODELSPAN = (,1992.DEC)
}
R-code:
seas(AirPassengers,
series.modelspan = ",1952.dec"
)
Remark(s):
- The
composite
spec is not supported. Definingcomptype
has no ueseful effect.
Example 7
SERIES{ TITLE = "Monthly data in a date-value format"
PERIOD = 12
FILE = "C:\DATA\SALES1.EDT" # a DOS path and file
FORMAT = "DATEVALUE"
}
R-code:
seas(AirPassengers)
Remark(s):
- Time series specification is handled by
seasonal
. No manual input needed.
Example 8
SERIES{ TITLE = "Monthly data in a file saved by \thisprogram\ "
PERIOD = 12
FILE = "C:\DATA\SALES1.A11" # a DOS path and file
FORMAT = "X13SAVE" }
R-code:
seas(AirPassengers)
Remark(s):
- Time series specification is handled by
seasonal
. No manual input needed.
Example 9
SERIES{ TITLE = "Monthly data in the comma variant of datevalue format"
PERIOD = 12
FILE = "C:\DATA\SALES1C.EDT" # a DOS path and file
FORMAT = "DATEVALUECOMMA" }
R-code:
seas(AirPassengers)
Remark(s):
- Time series specification is handled by
seasonal
. No manual input needed.
7.16 SLIDINGSPANS
Example 1
SERIES { FILE = "TOURIST.DAT" START = 1976.1 }
X11 { SEASONALMA = S3X9 }
SLIDINGSPANS { }
R-code:
m <- seas(AirPassengers,
x11.seasonalma = "S3X9"
)
out(m)
series(m, "slidingspans.sfspans")
Remark(s):
- With the HTML version, the output can be analyzed in the browser.
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 2
Series {
File = "qstocks.dat"
Start = 1967.1
Title = "Quarterly stock prices on NASDAC"
Freq = 4
}
X11 {
Seasonalma = ( S3x9 S3x9 S3x5 S3x5 )
Trendma = 7
Mode = Logadd
}
Slidingspans {
cutseas = 5.0
cutchng = 5.0
}
R-code:
m <- seas(JohnsonJohnson,
transform.function = "log",
x11.seasonalma = c("S3x9", "S3x9", "S3x5", "S3x5"),
x11.trendma = 7,
x11.mode = "logadd",
slidingspans.cutseas = 5,
slidingspans.cutchng = 5
)
out(m)
series(m, "slidingspans.sfspans")
Remark(s):
- With the HTML version, the output can be analyzed in the browser.
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 3
series { title = "Number of employed machinists - X-11"
start = 1980.jan file = "machine.emp"
}
regression { variables = (const td rp82.may-82.oct) } arima {model=(012)(011)}
outlier {}
estimate {}
check {}
forecast {}
x11 { mode = add save = d11}
slidingspans { outlier = keep
length = 144 }
R-code:
m <- seas(AirPassengers,
regression.variables = c("const", "td", "rp1952.may-1952.oct"),
arima.model = "(0 1 2)(0 1 1)",
x11.mode = "add",
transform.function = "none",
slidingspans.outlier = "keep",
slidingspans.length = 50
)
series(m, "slidingspans.sfspans")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 4
series { title = "Number of employed machinists - SEATS"
start = 1980.jan file = "machine.emp"
}
regression { variables = (const td rp82.may-82.oct) } arima {model=(012)(011)}
outlier {}
estimate {}
check {}
forecast {}
seats { save = s11 }
slidingspans { outlier = keep
length = 144 }
R-code:
m <- seas(AirPassengers,
regression.variables = c("const", "td", "rp1952.may-1952.oct"),
arima.model = "(0 1 2)(0 1 1)",
slidingspans.outlier = "keep",
slidingspans.length = 50
)
series(m, "slidingspans.sfspans")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 5
series { title = "Cheese Sales in Wisconsin"
file = "cheez.fil" start = 1975.1 }
transform { function = log }
regression { variables = (const seasonal tdnolpyear) } arima{ model=(310) }
forecast { maxlead = 60 }
x11 { save = seasonal appendfcst = yes } slidingspans { fixmdl = no }
R-code:
m <- seas(AirPassengers,
transform.function = "log",
regression.variables = c("const", "seasonal", "tdnolpyear"),
arima.model = "(3 1 0)",
x11.appendfcst = "yes",
slidingspans.fixmdl = "no"
)
series(m, "slidingspans.sfspans")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
Example 6
Series {
File = "qstocks.dat"
Start = 1987.1
Title = "Quarterly stock prices on NASDAC"
Freq = 4
}
X11 {
Seasonalma = S3x9
}
Slidingspans {
Length = 40
Numspans = 3 }
R-code:
m <- seas(AirPassengers,
x11.seasonalma = "S3X9",
slidingspans.length = 40,
slidingspans.numspans = 3
)
series(m, "slidingspans.sfspans")
Remark(s):
- the
series
function imports all tables that can be saved in X-13ARIMA-SEATS.
7.17 SPECTRUM
Example 1
series{ title = "Spectrum analysis of Building Permits Series"
start = 1967.Jan
file = "permits.dat"
format = "(12f6.0)"
print = none
} transform{
function = log
print = none }
spectrum{
start = 1987.Jan
print = (none +specorig)
savelog = all
}
R-code:
m <- seas(AirPassengers,
transform.function = "log",
spectrum.start = "1952.jan",
spectrum.print = "specorig",
spectrum.savelog = "all"
)
out(m)
Remark(s):
- With the HTML version, the
log
output can be analyzed in the browser with theout()
function. Click onLog Entry
in the sidebar.
Example 2
composite { title="TOTAL ONE-FAMILY Housing Starts"
name="C1FTHS" save=(indseasonal) }
x11 { seasonalma=(s3x9)
title="Composite adj. of 1-Family housing starts"
save=(D10) }
spectrum { savelog = (indpeaks indqs)
type = periodogram
save = is1 }
Remark(s):
- The
composite
spec is not supported.
7.18 TRANSFORM
Example 1
series { data = (879 899 462 670 985 973 ...)
start = 1967.jan }
transform{data =(1 1.5.75 1 1...) mode = ratio
adjust = lom }
R-code:
# adjustment series
tf <- ts(runif(250), start = c(1945, 1), frequency = 12)
m <- seas(AirPassengers,
x11 = "",
xtrans = tf,
transform.mode = "ratio",
transform.adjust = "lom",
transform.function = "log",
regression.aictest = NULL
)
Remark(s):
- Length-of-month prior adjustment cannot be specified when td is given in the aictest argument of the regression spec.
Example 2
series { data = (6 79 98 42 4 73 85 26 ...)
start = 1997.1 period=4 }
transform { constant=45 function = auto }
R-code:
m <- seas(AirPassengers,
transform.constant = 45
)
Remark(s):
transform.function = "auto"
is the default setting inseas
.
Example 3
series {title = "Total U.S. Retail Sales --- Current Dollars"
file = "retail.dat"
start = 1980.jan }
transform {function = log
title = "Consumer Price Index"
start = 1970.jan # adj. factors start January, 1970
file = "cpi.dat"
format = "(12f6.3)" }
R-code:
# adjustment series
tf <- ts(runif(250), start = c(1945, 1), frequency = 12)
m <- seas(AirPassengers,
x11 = "",
xtrans = tf,
transform.function = "log"
)
Example 4
series {title = "Total U.S. Retail Sales --- Current Dollars"
file = "retail.dat"
start = 1980.jan }
transform {function = log
title = "Consumer Price Index"
start = 1970.jan # adj. factors start January, 1970
file = "cpi.dat"
format = "1R"
precision = 3
name = "cpi"
type = temporary
}
R-code:
# adjustment series
tf <- ts(runif(250), start = c(1945, 1), frequency = 12)
m <- seas(AirPassengers,
x11 = "",
xtrans = tf,
transform.type = "temporary",
transform.function = "log"
)
Example 5
SERIES {TITLE="Annual Rainfall"
FILE="RAIN.DAT"
PERIOD=4
START=1901.1}
TRANSFORM {POWER=.3333}
R-code:
m <- seas(AirPassengers,
transform.function = "none",
transform.power = 0.3333
)
Example 6
series {title = "Retail Sales of computers --- Current Dollars"
file = "rscomp.dat" start = 1980.jan
}
transform { function = log
title = "Consumer Price Index & Strike Effect"
type = (permanent temporary)
start = 1970.jan # adj. factors start January, 1970
file = ("cpi.dat" "strike.dat")
format = "1R" precision = 3
name = ("cpi" "strike")
}
R-code:
# temporary and permanent adjustment
cpi <- ts(runif(250), start = c(1945, 1), frequency = 12)
strike <- ts(runif(250), start = c(1945, 1), frequency = 12)
m <- seas(AirPassengers,
xtrans = cbind(cpi, strike),
transform.type = c("temporary", "permanent"),
transform.function = "log"
)
Example 7
series {title = "Total U.K. Retail Sales"
file = "ukretail.dat"
start = 1978.jan
}
transform {function = auto
aicdiff = 0.0
}
R-code:
m <- seas(AirPassengers,
transform.aicdiff = 0.0
)
Remark(s):
transform.function = "auto"
is the default setting inseas
.
7.19 X11
Example 1
Series { File="klaatu.dat" Start = 1976.1 }
X11 { }
R-code:
seas(AirPassengers,
x11 = ""
)
Remark(s):
- If the
x11
spec is specified (here, as an empty spec), the defaultseats
spec is automatically disabled.
Example 2
X11 { SeasonalMA = s3x9 TrendMA = 23 }
X11regression { variables = td aictest=td }
R-code:
seas(AirPassengers,
regression.aictest = NULL,
x11.seasonalma = "s3x9",
x11.trendma = 23,
x11regression.variables = "td",
x11regression.aictest = "td"
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
Example 3
series {
file="qhstarts.dat"
start = 1967.1
period=4 }
x11 {
seasonalma = (s3x3 s3x3 s3x5 s3x5)
trendma = 7
}
R-code:
# we have monthly data
seas(JohnsonJohnson,
x11.seasonalma = c("s3x3", "s3x3", "s3x5", "s3x5"),
x11.trendma = 7
)
Example 4
SERIES { TITLE = "EXPORTS OF LEATHER GOODS" START = 1969.JUL
DATA = (815 866 926 ... 942) }
REGRESSION { VARIABLES = (CONST TD LS1972.MAY LS1976.OCT) }
ARIMA { MODEL=(0 1 2)(1 1 0) }
ESTIMATE { }
FORECAST { MAXLEAD=0 }
X11 { MODE = ADD PRINT = ALLTABLES SIGMALIM = (2.0 3.5) }
R-code:
seas(AirPassengers,
transform.function = "none",
regression.variables = c("const", "td", "ls1960.may", "ls1960.oct"),
arima.model = "(0 1 2)(1 1 0)",
forecast.maxlead = 0,
x11.mode = "add",
x11.sigmalim = c(2.0, 3.5)
)
Remark(s):
- Automatic transformation has to be turned off.
Example 5
series { title = "Unit Auto Sales" file = "autosal.dat"
start = 1985.1 }
transform { function = log }
regression { variables = (const td)
user = (sale88 sale90)
file = "special.dat" format = "(2f12.2)" } arima {model=(310)(011)12 }
forecast { maxlead=12 maxback=12 }
x11 { title = ( "Unit Auto Sales"}
R-code:
seas(AirPassengers,
transform.function = "none",
regression.variables = c("const", "td", "ls1960.may", "ls1960.oct"),
arima.model = "(0 1 2)(1 1 0)",
forecast.maxlead = 0,
x11.mode = "add",
x11.sigmalim = c(2.0, 3.5)
)
Example 6
series { title="NORTHEAST ONE FAMILY Housing Starts"
file="cne1hs.ori" name="CNE1HS" format="2R" }
transform { function=log }
regression {
variables=(ao1976.feb ao1978.feb ls1980.feb
ls1982.nov ao1984.feb)
}
arima { model=(0 1 2)(0 1 1) }
forecast { maxlead=60 }
x11 { seasonalma=(s3x9)
title="Adjustment of 1 family housing starts"
save = e2
}
R-code:
seas(AirPassengers,
transform.function = "log",
regression.variables = c("ao1956.feb", "ao1958.feb", "ls1960.feb",
"ls1952.nov"),
arima.model = "(0 1 2)(0 1 1)",
forecast.maxlead = 60,
x11.seasonalma = "s3x9"
)
7.20 X11REGRESSION
Example 1
Series { File = "westus.dat"
Start = 1976.1
} X11 { }
X11Regression { Variables = td
}
R-code:
m <- seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
x11regression.variables = "td"
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.
Example 2
Series { File = "westus.dat"
Start = 1976.1
} X11 { }
X11Regression { Variables = td
Aictest = (td easter)
}
R-code:
m <- seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
x11regression.variables = "td",
x11regression.aictest = c("td", "easter")
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.
Example 3
series {
file = "ukclothes.dat"
start = 1985.Jan
}
x11 { }
x11regression{
variables = td
outler = 4.0
user = (easter1 easter2) file = "ukeaster.dat"
usertype = holiday start = 1980.Jan
}
R-code:
data(holiday)
easter1 <- genhol(easter, start = -10, end = -1, frequency = 12)
easter2 <- genhol(easter, start = 0, end = 5, frequency = 12)
seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
xreg = cbind(easter1, easter2),
x11regression.aictest = "td",
x11regression.usertype = "holiday",
outlier = NULL
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.
Example 4
series {
file = "nzstarts.dat" start = 1980.Jan
}
x11 { }
x11regression{
variables = td
tdprior = (1.4 1.4 1.4 1.4 1.4 0.0 0.0)
}
R-code:
seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
x11regression.variables = "td",
x11regression.tdprior = c(1.4, 1.4, 1.4, 1.4, 1.4, 0.0, 0.0),
transform.function = "log"
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.
Example 5
series{
format = ’2R’
title = ’MIDWEST ONE FAMILY Housing Starts’
name = ’CMW1HS’
file = ’cmw1hs.ori’
span = (1964.01,1989.03)
}
x11{ }
x11regression{
variables = (td easter[8])
b = ( 0.4453f 0.8550f -0.3012f 0.2717f
-0.1705f 0.0983f -0.0082)
}
R-code:
seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
x11regression.variables = c("td", "easter[8]"),
x11regression.critical = 5,
x11regression.b = c("0.4f", "0.8f", "-0.3f", "0.2f",
"-0.1f", "0.1f", "-0.1")
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.
Example 6
series{
title = ’Motor Home Sales’
start = 1967.1
span = (1972.1, )
name = ’SB0562’
file = ’C:\final.x12\T0B05601.TXT’
format = ’2L’
}
X11REGRESSION { variables = ( td/1990.1/
easter[8] labor[10] thank[10] ) }
x11{
seasonalma = x11default
sigmalim = (1.8 2.8)
appendfcst = YES
save = (D11 D16)
}
R-code:
seas(AirPassengers,
x11 = "",
regression.aictest = NULL,
x11regression.variables = c("td/1950.1/", "easter[8]",
"labor[10]", "thank[10]"),
x11.seasonalma = "x11default",
x11.sigmalim = c(1.8, 2.9),
x11.appendfcst = "yes",
)
Remark(s):
- Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.
Example 7
series{ title = "Automobile Sales"
file = "carsales.dat"
start = 1975.Jan }
transform{ function = log }
regression{ variables = (const)
user = (strike80 strike85 strike90)
file = "strike.dat"
format = "(3f12.0)" }
arima{ model = (0 1 1)(0 1 1)12 }
x11{ title = ("Car Sales in US"
"Adjust for strikes in 80, 85, 90")
save = seasonal appendfcst = yes
}
x11regression{ variables = ( td easter[8] ) }
R-code:
seas(AirPassengers,
x11 = "",
transform.function = "log",
regression.variables = "const",
regression.aictest = NULL,
arima.model = "(0 1 1)(0 1 1)",
outlier = NULL,
x11regression.variables = c("td", "easter[8]")
)
Remark(s):
- For an unknown X-13-ARIMA SEATS related reason, user defined regressors do not work well with both
x11regression
andregression
specified. x11regression
regressors are not shown insummary
. Useout(m)
to inspect the regression output.