Examples from the X-13 manual

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)
)

Run Online

Remark(s):

If the x11 spec is specified (here, as an empty spec), the default seats 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)"
)

Run Online

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)"
)

Run Online

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)"
)

Run Online

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"
)

Run Online

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)"
)

Run Online

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"
)

Run Online

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")
)

Run Online

Remark(s):

If the x11 spec is specified (here, as an empty spec), the default seats 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, "
)

Run Online

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 the out() function. Click on Log 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)

Run Online

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)

Run Online

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)

Run Online

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"
)

Run Online

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"
)

Run Online

Remark(s):

Instead of saving the .mdl file as an input, users should directly save the call to seas.

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"
     )

Run Online

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
)

Run Online

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"
)

Run Online

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")

Run Online

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")

Run Online

Remark(s):

the series function imports all tables that can be saved in X-13ARIMA-SEATS.
The estimate and outlier 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")

Run Online

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
)

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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)

Run Online

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)
)

Run Online

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)

Run Online

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
)

Run Online

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")
)

Run Online

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
)

Run Online

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"
)

Run Online

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"
)

Run Online

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"
)

Run Online

Remark(s):

If the x11 spec is specified (here, as an empty spec), the default seats spec is automatically disabled.
If the pickmdl spec is specified, the default automdl 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"
)

Run Online

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"
)

Run Online

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)"
)

Run Online

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)

Run Online

Remark(s):

With the HTML version, the log output can be analyzed in the browser with the out() function. Click on Log 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)
)

Run Online

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)"
)

Run Online

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 = ""
)

Run Online

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)"
)

Run Online

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)"
)

Run Online

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)

Run Online

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"
)

Run Online

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 = ""
)

Run Online

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 = ""
)

Run Online

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)"
)

Run Online

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)"
)

Run Online

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)"
)

Run Online

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"
)

Run Online

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"
)

Run Online

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 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
)

Run Online

Remark(s):

transform.function = "auto", and automdl = "" and seats = "" 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)

Run Online

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")

Run Online

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)

Run Online

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)

Run Online

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"
     )

Run Online

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"
     )

Run Online

Remark(s):

The composite spec is not supported. Defining comptype 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)

Run Online

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)

Run Online

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)

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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")

Run Online

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)

Run Online

Remark(s):

With the HTML version, the log output can be analyzed in the browser with the out() function. Click on Log 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
)

Run Online

Remark(s):

transform.function = "auto" is the default setting in seas.

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
)

Run Online

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
)

Run Online

Remark(s):

transform.function = "auto" is the default setting in seas.

7.19 X11

Example 1

Series { File="klaatu.dat" Start = 1976.1 }
X11 {  }

R-code:

seas(AirPassengers, 
     x11 = ""
)

Run Online

Remark(s):

If the x11 spec is specified (here, as an empty spec), the default seats 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"
)

Run Online

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
)

Run Online

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)
)

Run Online

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)
)

Run Online

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"
)

Run Online

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"
)

Run Online

Remark(s):

Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression regressors are not shown in summary. Use out(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")
)

Run Online

Remark(s):

Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression regressors are not shown in summary. Use out(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 in summary. Use out(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"
)

Run Online

Remark(s):

Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression regressors are not shown in summary. Use out(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")
     )

Run Online

Remark(s):

Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression regressors are not shown in summary. Use out(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",
     )

Run Online

Remark(s):

Irregular component regression and regARIMA model-based trading day adjustment cannot be specified in the same run.
x11regression regressors are not shown in summary. Use out(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]")
)

Run Online

Remark(s):

For an unknown X-13-ARIMA SEATS related reason, user defined regressors do not work well with both x11regression and regression specified.
x11regression regressors are not shown in summary. Use out(m) to inspect the regression output.