Type:	Package
Title:	Seasonal Adjustment with TRAMO-SEATS in 'JDemetra+' 3.x
Version:	3.6.0
Description:	Interface to 'JDemetra+' 3.x (https://github.com/jdemetra) time series analysis software. It offers full access to options and outputs of 'TRAMO-SEATS' (Time series Regression with ARIMA noise, Missing values and Outliers - Signal Extraction in ARIMA Time Series), including 'TRAMO' modelling (ARIMA model with outlier detection and trading days adjustment). ARIMA = AutoRegressive Integrated Moving Average.
License:	EUPL version 1.1 | EUPL version 1.2 [expanded from: EUPL]
URL:	https://github.com/rjdverse/rjd3tramoseats, https://rjdverse.github.io/rjd3tramoseats/
BugReports:	https://github.com/rjdverse/rjd3tramoseats/issues
Depends:	R (≥ 4.1.0)
Imports:	rJava (≥ 1.0-6), rjd3jars, rjd3toolkit (≥ 3.6.0), RProtoBuf (≥ 0.4.20), stats, utils
Encoding:	UTF-8
RoxygenNote:	7.3.3
SystemRequirements:	Java (>= 17)
Collate:	'deprecated.R' 'print.R' 'utils.R' 'tramoseats_rslts.R' 'tramoseats_spec.R' 'revisions.R' 'seats.R' 'seats_spec.R' 'tramo_generic.R' 'tramo_outliers.R' 'tramo_spec.R' 'tramoseats.R' 'zzz.R'
Suggests:	spelling
Language:	en-US
NeedsCompilation:	no
Packaged:	2026-01-23 16:02:22 UTC; onyxia
Author:	Jean Palate [aut], Alain Quartier-la-Tente [aut], Tanguy Barthelemy [aut, cre, art], Anna Smyk [aut]
Maintainer:	Tanguy Barthelemy <tanguy.barthelemy@insee.fr>
Repository:	CRAN
Date/Publication:	2026-01-27 21:20:02 UTC

Deprecated functions

Description

Deprecated functions

Usage

fast_tramoseats(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

fast_tramo(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  context = NULL,
  userdefined = NULL
)

spec_tramoseats(
  name = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5")
)

spec_tramo(name = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"))

userdefined_variables_tramoseats(x = c("TRAMO-SEATS", "TRAMO"))

Arguments

Value

All these functions are deprecated and return the same value as the function that replaces them:

• spec_tramoseats() returns the same value as tramoseats_spec()

• spec_tramo() returns the same value as tramo_spec()

• fast_tramoseats() returns the same value as tramoseats_fast()

• fast_regarima() returns the same value as regarima_fast()

• .jtramoseats() returns the same value as jtramoseats()

• userdefined_variables_tramoseats() returns the same value as tramoseats_dictionary()

Java version.

Description

Java version.

Usage

current_java_version

minimal_java_version

Format

An object of class integer of length 1.

An object of class numeric of length 1.

Value

current_java_version is the current Java version and minimal_java_version is the minimum accepted Java version.

Examples

print(minimal_java_version)
print(current_java_version)

Java Utility Functions

Description

These functions are used in all JDemetra+ 3.0 packages to easily interact between R and Java objects.

Usage

.tramoseats_rslts(jrslts)

.jd2r_spec_tramo(jspec)

.r2jd_spec_tramo(spec)

.jd2r_spec_tramoseats(jspec)

.r2jd_spec_tramoseats(spec)

Arguments

Value

These functions return specification in Java, proto or R.

Refresh a specification with constraints

Description

Functions tramoseats_refresh() and tramo_refresh() allow to create a new specification by updating a specification used for a previous estimation. Some selected parameters will be kept fixed (previous estimation results) while others will be freed for re-estimation in a domain of constraints. See details and examples.

Usage

tramo_refresh(
  spec,
  refspec = NULL,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"),
  period = 0,
  start = NULL,
  end = NULL
)

tramoseats_refresh(
  spec,
  refspec = NULL,
  policy = c("FreeParameters", "Complete", "Outliers_StochasticComponent", "Outliers",
    "FixedParameters", "FixedAutoRegressiveParameters", "Fixed", "Current"),
  period = 0,
  start = NULL,
  end = NULL
)

Arguments

Details

The selection of constraints to be kept fixed or re-estimated is called a revision policy. User-defined parameters are always copied to the new refreshed specifications. This revision applies to the estimation done in Tramo (pre-adjustment phase), Seats will then run a new decomposition which might be in some (rare) cases based on a different model.

Available refresh policies are:

Current: applying the current pre-adjustment reg-arima model and handling the new raw data points, or any sub-span of the series as Additive Outliers (defined as new intervention variables)

Fixed: applying the current pre-adjustment reg-arima model and replacing forecasts by new raw data points.

FixedParameters: pre-adjustment reg-arima model is partially modified: regression coefficients will be re-estimated but regression variables, Arima orders and coefficients are unchanged.

FixedAutoRegressiveParameters: same as FixedParameters but Arima Moving Average coefficients (MA) are also re-estimated, Auto-regressive (AR) coefficients are kept fixed.

FreeParameters: all regression and Arima model coefficients are re-estimated, regression variables and Arima orders are kept fixed.

Outliers: regression variables and Arima orders are kept fixed, but outliers will be re-detected on the defined span, thus all regression and Arima model coefficients are re-estimated

Outliers_StochasticComponent: same as "Outliers" but Arima model orders (p,d,q)(P,D,Q) can also be re-identified.

Complete: All the parameters are re-identified and re-estimated, unless constrained in the domain spec.

Value

a new specification, an object of class "JD3_TRAMOSEATS_SPEC" or "JD3_TRAMO_SPEC".

References

More information on revision policies in JDemetra+ online documentation: https://jdemetra-new-documentation.netlify.app/a-rev-policies

Examples

y <- rjd3toolkit::ABS$X0.2.08.10.M
# raw series for first estimation
y_raw <- window(y, end = c(2016, 12))
# raw series for second (refreshed) estimation
y_new <- window(y, end = c(2017, 6))

# specification for first estimation
spec_tramoseats_1 <- tramoseats_spec("rsafull")

# first estimation
sa_tramoseats <- tramoseats(y_raw, spec_tramoseats_1)
# refreshing the specification
current_result_spec <- sa_tramoseats$result_spec
current_domain_spec <- sa_tramoseats$estimation_spec

# policy = "Fixed"
spec_tramoseats_ref <- tramoseats_refresh(current_result_spec, # point spec to be refreshed
    current_domain_spec, # domain spec (set of constraints)
    policy = "Fixed"
)

# 2nd estimation with refreshed specification
sa_tramoseats_ref <- tramoseats(y_new, spec_tramoseats_ref)
# policy = "Outliers"
spec_tramoseats_ref <- tramoseats_refresh(current_result_spec,
    current_domain_spec,
    policy = "Outliers",
    period = 12,
    start = c(2017, 1)
) # outliers will be re-detected from January 2017 included
# 2nd estimation with refreshed specification
sa_tramoseats_ref <- tramoseats(y_new, spec_tramoseats_ref)

# policy = "Current"
spec_tramoseats_ref <- tramoseats_refresh(current_result_spec,
    current_domain_spec,
    policy = "Current",
    period = 12,
    start = c(2017, 1),
    end = end(y_new)
)
# points from January 2017 (included) until the end of the series will be treated
# as Additive Outliers, the previous reg-Arima model being otherwise kept fixed

# 2nd estimation with refreshed specification
sa_tramoseats_ref <- tramoseats(y_new, spec_tramoseats_ref) #'
# same procedure using tramo_refresh
# specification for first estimation
spec_1 <- tramo_spec("tr3")

# first estimation
tramo_model <- tramo(y_raw, spec_1)
tramo_model$estimation_spec

# refreshing the specification
current_result_spec <- tramo_model$result_spec
current_domain_spec <- tramo_model$estimation_spec

# policy = "Fixed"
spec_1_ref <- tramo_refresh(current_result_spec, # point spec to be refreshed
                             current_domain_spec, # domain spec (set of constraints)
                              policy = "Fixed"
                               )

# 2nd estimation with refreshed specification
tramo_model_ref <- tramo(y_new, spec_1_ref)

SEATS Decomposition

Description

SEATS Decomposition

Usage

seats_decompose(
  sarima,
  seas.tolerance = 2,
  trend.boundary = 0.5,
  seas.boundary = 0.8,
  seas.boundary.unique = 0.8,
  approximation = c("None", "Legacy", "Noisy")
)

Arguments

Value

returns a "JD3_UCARIMA" object

Examples

seats_decompose(rjd3toolkit::sarima_model(period = 12, phi = c(0, 1), bd = 1))

Set Seats Specification

Description

Function allowing to customize parameters in the decomposition part (Seats) of a Tramo-Seats seasonal adjustment process. (Seats is an Arima Model Based decomposition algorithm working in conjunction with Tramo.)

Usage

set_seats(
  x,
  approximation = c(NA, "None", "Legacy", "Noisy"),
  trend.boundary = NA,
  seas.boundary = NA,
  seas.boundary.unique = NA,
  seas.tolerance = NA,
  ma.boundary = NA,
  fcasts = NA,
  bcasts = NA,
  algorithm = c(NA, "Burman", "KalmanSmoother"),
  bias = NA
)

Arguments

Value

an object of class "JD3_TRAMOSEATS_SPEC".

References

More information and examples related to 'JDemetra+' features in the online documentation: https://jdemetra-new-documentation.netlify.app/

See Also

tramoseats_spec().

Examples

init_spec <- tramoseats_spec("rsafull")
new_spec <- set_seats(init_spec,
    approximation = "Legacy",
    trend.boundary = 0.8,
    seas.boundary = 0.5,
    fcasts = -3,
    algorithm = "KalmanSmoother",
    bias = TRUE
)
y <- rjd3toolkit::ABS$X0.2.09.10.M

sa <- tramoseats(y, spec = new_spec)

TERROR Quality Control of Outliers

Description

TRAMO for ERRORs (TERROR) controls the quality of the data by checking outliers at the end of the series

Usage

terror(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  nback = 1,
  context = NULL
)

Arguments

Value

a mts object with 7 variables:

1. actual: the actual data at the end of the series;

2. forecast: the forecast of the actual data at the end of the series;

3. error: the absolute errors (= observed - forecasts);

4. rel.error: relative errors ("scores") : ratios between the forecast errors and the standard deviation of the forecasts of the last observations (positive values mean under-estimation);

5. raw: the transformed series. More especially, if the chosen model implies a log-transformation, the values are obtained after a log-transformation. Other transformations, such leap year corrections or length-of periods corrections may also be used;

6. fraw: the forecast of the transformed series.;

7. efraw: the absolute errors of the transformed series.

Examples

terror(rjd3toolkit::ABS$X0.2.09.10.M, nback = 2)

TRAMO model, pre-adjustment in TRAMO-SEATS

Description

allows to model the series with a Reg-Arima model, estimate outlier, calendar or other regression effects and produce forecasts

Usage

tramo(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  context = NULL,
  userdefined = NULL
)

tramo_fast(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  context = NULL,
  userdefined = NULL
)

Arguments

Value

the tramo() function returns a list with the results ("JD3_tramo_rslts" object), the estimation specification and the result specification, while tramo_fast() is a faster function that only returns the results.

Examples

library("rjd3toolkit")

y <- rjd3toolkit::ABS$X0.2.09.10.M
sp <- tramo_spec("trfull")
sp <- add_outlier(sp,
    type = c("AO"), c("2015-01-01", "2010-01-01")
)

tramo_fast(y, spec = sp)

sp <- set_transform(
    set_tradingdays(
        set_easter(sp, enabled = FALSE),
        option = "workingdays"
    ),
    fun = "None"
)

tramo_fast(y, spec = sp)

sp <- set_outlier(sp, outliers.type = c("AO"))


tramo_fast(y, spec = sp)

Forecasts with TRAMO

Description

Forecasts with TRAMO

Usage

tramo_forecast(
  ts,
  spec = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"),
  nf = -1,
  context = NULL
)

Arguments

Value

a mts object with 7 variables:

• forecast the forecast of the actual data at the end of the series.

• error standard deviation of the forecast.

• fraw the forecast of the transformed series.

• efraw the standard deviation of the forecast of the transformed series.

Examples

tramo_forecast(rjd3toolkit::ABS$X0.2.09.10.M)

Outlier Detection with a Tramo Model

Description

Tramo is a particular regarima model estimation algorithm, mainly used to linearized the series before performing a decomposition with Seats

Usage

tramo_outliers(
  y,
  order = c(0L, 1L, 1L),
  seasonal = c(0L, 1L, 1L),
  mean = FALSE,
  X = NULL,
  X.td = NULL,
  ao = TRUE,
  ls = TRUE,
  tc = FALSE,
  so = FALSE,
  cv = 0,
  ml = FALSE,
  clean = FALSE
)

Arguments

Value

a "JD3_REGARIMA_OUTLIERS" object.

Examples

tramo_outliers(rjd3toolkit::ABS$X0.2.09.10.M)

Seasonal Adjustment with TRAMO-SEATS

Description

Seasonal Adjustment with TRAMO-SEATS

Usage

tramoseats(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

tramoseats_fast(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

.jtramoseats(
  ts,
  spec = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5"),
  context = NULL,
  userdefined = NULL
)

Arguments

Value

The tramoseats() function returns a list with the results, the estimation specification and the result specification, while tramoseats_fast() is a faster function that only returns the results. The .jtramoseats() functions only results the java object to custom outputs in other packages (use rjd3toolkit::dictionary() to get the list of variables and rjd3toolkit::result() to get a specific variable).

Examples

library("rjd3toolkit")

sp <- tramoseats_spec("rsafull")
y <- rjd3toolkit::ABS$X0.2.09.10.M

tramoseats(y, spec = sp)
tramoseats_fast(y, spec = sp)

sp <- add_outlier(sp,
    type = c("AO"), c("2015-01-01", "2010-01-01")
)
sp <- set_transform(
    set_tradingdays(
        set_easter(sp, enabled = FALSE),
        option = "workingdays"
    ),
    fun = "None"
)

tramoseats(y, spec = sp)
tramoseats_fast(y, spec = sp)

TRAMO-SEATS dictionary

Description

Functions to provide information for all output objects (series, diagnostics, parameters) available with tramoseats() function.

Usage

tramoseats_dictionary()

tramoseats_full_dictionary()

Details

These functions provide lists of output names (series, diagnostics, parameters) available with the tramoseats() function. These names can be used to generate customized outputs with the userdefined option of the tramoseats() function (see examples). The tramoseats_full_dictionary function provides additional information on object format and description.

Value

tramoseats_dictionary() returns a character vector containing the names of all output objects (series, diagnostics, parameters) available with the tramoseats() function, whereas tramoseats_full_dictionary() returns a data.frame with format and description, for all the output objects.

Examples

# Visualize the dictionary
print(tramoseats_dictionary())
summary(tramoseats_dictionary())

# first 10 lines
head(tramoseats_full_dictionary(), n = 10)
# For more structured information call `View(tramoseats_full_dictionary())`

# Extract names of output of interest
user_defined_output <- tramoseats_dictionary()[c(65, 95, 135)]
user_defined_output

# Generate the corresponding output in an estimation
y <- rjd3toolkit::ABS$X0.2.09.10.M

m <- tramoseats(y, "rsafull", userdefined=user_defined_output)

# Retrieve user defined output
tail(m$user_defined$ylin)
m$user_defined$residuals.kurtosis
m$user_defined$sa_f

Revisions History

Description

Computes revisions history

Usage

tramoseats_revisions(
  ts,
  spec,
  data_ids = NULL,
  ts_ids = NULL,
  cmp_ids = NULL,
  context = NULL
)

Arguments

Value

returns a list

Examples

s <- rjd3toolkit::ABS$X0.2.09.10.M
sa_mod <- tramoseats(s)
data_ids <- list(
    # Get the coefficient of the trading-day coefficient from 2005-jan
    list(start = "2005-01-01", id = "regression.td(1)"),
    # Get the ljung-box statistics on residuals from 2010-jan
    list(start = "2010-01-01", id = "residuals.lb")
)
ts_ids <- list(
    # Get the SA component estimates of 2010-jan from 2010-jan
    list(period = "2010-01-01", start = "2010-01-01", id = "sa"),
    # Get the irregular component estimates of 2010-jan from 2015-jan
    list(period = "2010-01-01", start = "2015-01-01", id = "i")
)
cmp_ids <- list(
    # Get the SA component estimates (full time series) 2010-jan to 2020-jan
    list(start = "2010-01-01", end = "2020-01-01", id = "sa"),
    # Get the trend component estimates (full time series)  2010-jan to 2020-jan
    list(start = "2010-01-01", end = "2020-01-01", id = "t")
)
rh <- tramoseats_revisions(s, sa_mod$result_spec, data_ids, ts_ids, cmp_ids)

TRAMO/TRAMO-SEATS Default Specification

Description

Set of functions(tramoseats_spec(),tramo_spec()) to create default specifications associated with the TRAMO-SEATS seasonal adjustment method. Specification creation can be restricted to the tramo part with the tramo_spec() function.

Usage

tramo_spec(name = c("trfull", "tr0", "tr1", "tr2", "tr3", "tr4", "tr5"))

tramoseats_spec(
  name = c("rsafull", "rsa0", "rsa1", "rsa2", "rsa3", "rsa4", "rsa5")
)

Arguments

Details

Without argument tramo_spec() yields a TR5 specification

without argument tramoseats_spec() yields a RSA5 specification

The available predefined 'JDemetra+' model specifications are described in the table below:

Value

an object of class "JD3_TRAMOSEATS_SPEC" (tramoseats_spec()) or "JD3_TRAMO_SPEC" (tramo_spec()).

See Also

1. To set the pre-processing parameters: rjd3toolkit::set_arima(), rjd3toolkit::set_automodel(), rjd3toolkit::set_basic(), rjd3toolkit::set_easter(), rjd3toolkit::set_estimate(), rjd3toolkit::set_outlier(), rjd3toolkit::set_tradingdays(), rjd3toolkit::set_transform(), rjd3toolkit::add_outlier(), rjd3toolkit::remove_outlier(), rjd3toolkit::add_ramp(), rjd3toolkit::remove_ramp(), rjd3toolkit::add_usrdefvar();

2. To set the decomposition parameters: set_seats();

3. To set the benchmarking parameters: rjd3toolkit::set_benchmarking().

Examples

init_spec <- tramoseats_spec()
init_spec <- tramo_spec()
init_spec <- tramoseats_spec("rsa3")
init_spec <- tramo_spec("tr3")