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
x11spec is specified (here, as an empty spec), the defaultseatsspec 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.maargument 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
x11spec is specified (here, as an empty spec), the defaultseatsspec 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.maxorderargument 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
logoutput can be analyzed in the browser with theout()function. Click onLog Entryin 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
compositespec 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.aictesthas 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
.mdlfile 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
seriesfunction 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
seriesfunction imports all tables that can be saved in X-13ARIMA-SEATS. - The
estimateandoutlierspec 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction imports all tables that can be saved in X-13ARIMA-SEATS. - The
compositespec 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
x11spec is specified (here, as an empty spec), the defaultseatsspec is automatically disabled. - If the
pickmdlspec is specified, the defaultautomdlspec 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.aictesthas 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
logoutput can be analyzed in the browser with theout()function. Click onLog Entryin the sidebar. regression.aictesthas 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.aictesthas 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.aictesthas 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.aictesthas 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.aictesthas 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):
outlierhas 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.aictesthas 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.aictesthas 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.aictesthas 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.aictesthas 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
genholto 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
seriesfunction 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.aictesthas 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 directoryR-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
compositespec is not supported. Definingcomptypehas 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
seriesfunction 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
logoutput can be analyzed in the browser with theout()function. Click onLog Entryin 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
compositespec 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
x11spec is specified (here, as an empty spec), the defaultseatsspec 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.
x11regressionregressors 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.
x11regressionregressors 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.
x11regressionregressors 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.
x11regressionregressors 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.
x11regressionregressors 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.
x11regressionregressors 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
x11regressionandregressionspecified. x11regressionregressors are not shown insummary. Useout(m)to inspect the regression output.