Type:	Package
Title:	Automatic Univariate Time Series Modelling of many Kinds
Version:	5.1.5
Date:	2025-11-18
Author:	Diego J. Pedregal [aut, cre]
Maintainer:	Diego J. Pedregal <diego.pedregal@uclm.es>
Description:	Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.
License:	GPL-3
Encoding:	UTF-8
Imports:	ggplot2, gridExtra, tsibble, tsoutliers, stats, ggforce, utils, parallel
LinkingTo:	Rcpp, RcppArmadillo
Depends:	Rcpp (≥ 1.0.3), R (≥ 3.5.0)
LazyData:	true
Suggests:	knitr, rmarkdown
RoxygenNote:	7.3.3
NeedsCompilation:	yes
Packaged:	2025-11-18 10:55:21 UTC; diego.pedregal
Repository:	CRAN
Date/Publication:	2025-11-18 12:30:15 UTC

UComp

Description

Package for time series modelling and forecasting of times series models inspired on different sources:

Details

• Unobserved Components models due to A.C. Harvey (Basic Structural Model: BSM), enhanced with automatic identification tools by Diego J. Pedregal.

• ExponenTial Smoothing by R.J. Hyndman and colaborators.

• ARIMA models by V. Gómez and A. Maravall

• Tobit ETS models by Pedregal, Trapero and Holgado

The package is designed for automatic identification among a wide range of possible models. The models may include exogenous variables. ARMA irregular components and automatic detection of outliers in some instances.

References

Harvey AC (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cam- bridge University Press.

de Jong, P & Penzer, J (1998). Diagnosing Shocks in Time Series, Journal of the American Statistical Association, 93, 442, 796-806.

Pedregal, DJ, & Young, PC (2002). Statistical approaches to modelling and forecasting time series. In M. Clements, & D. Hendry (Eds.), Companion to economic forecasting (pp. 69–104). Oxford: Blackwell Publishers.

Durbin J, Koopman SJ (2012). Time Series Analysis by State Space Methods. 38. Oxford University Press.

Proietti T and Luati A (2013). Maximum likelihood estimation of time series models: the Kalman filter and beyond, in Handbook of research methods and applications in empirical macroeconomics, ed. Nigar Hashimzade and Michael Thornton, E. Elgar, UK.

Hyndman RJ, Koehler AB, Ord JK and Snyder RD (2008), Forecasting with exponential smoothing, The State Sapce approach, Berlin, Springer-Verlag.

Gómez V and Maravall, A (2000), Automatic methods for univariate series. In Peña, D., Tiao, G.C. and Tsay R.S., A course in time series analyis. Wiley.

Trapero JR, Holgado E, Pedregal DJ (2024), Demand forecasting under lost sales stock policies, International Journal of Forecasting, 40, 1055-1068.

Maintainer

Diego J. Pedregal

Author(s)

Diego J. Pedregal

AIC.UComp

Description

Extract AIC value of UComp object

Usage

## S3 method for class 'UComp'
AIC(object, ..., k = 2)

Arguments

Details

Selection criteria for models with different number of parameters, the smaller AIC the better. The formula used here is AIC=-2 (ln(L) - k) / n, where ln(L) is the log-likelihood at the optimum, k is the number of parameters plus non-stationary states and n is the number of observations. Mind that this formulation differs from the usual definition that does not divide by n. This makes that AIC(m) and AIC(logLik(m)) give different results, being m an UComp object.

Value

AIC value of a UC model

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents

Examples

y <- log(AirPassengers)
m1 <- UCforecast(y, model = "llt/equal/arma(0,0)")
AIC(m1)

ARIMA

Description

Runs all relevant functions for ARIMA modelling

Usage

ARIMA(
  y,
  u = NULL,
  model = NULL,
  cnst = NULL,
  s = frequency(y),
  criterion = "bic",
  h = 2 * s,
  verbose = FALSE,
  lambda = 1,
  maxOrders = c(3, 2, 3, 2, 1, 2),
  bootstrap = FALSE,
  nSimul = 5000,
  fast = FALSE
)

Arguments

Details

See help of ARIMAforecast.

Value

An object of class ARIMA. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ARIMA object as specified in what follows (function ARIMA fills in all of them at once):

After running ARIMAforecast or ARIMA:

After running ARIMAvalidate:

Author(s)

Diego J. Pedregal

See Also

ARIMAforecast, ARIMAvalidate,

Examples

y <- log(AirPassengers)
m1 <- ARIMA(y)
m1 <- ARIMA(y, lambda = NULL)

ARIMAestim

Description

Estimates and forecasts ARIMA models

Usage

ARIMAestim(m)

Arguments

Details

ARIMAestim estimates and forecasts a time series using an ARIMA model

Value

The same input object with the appropriate fields filled in, in particular:

ARIMAforecast

Description

Estimates and forecasts ARIMA general univariate models

Usage

ARIMAforecast(
  y,
  u = NULL,
  model = NULL,
  cnst = NULL,
  s = frequency(y),
  criterion = "bic",
  h = 2 * s,
  verbose = FALSE,
  lambda = 1,
  maxOrders = c(3, 2, 3, 2, 1, 2),
  bootstrap = FALSE,
  nSimul = 5000,
  fast = FALSE
)

Arguments

Details

ARIMAforecast is a function for modelling and forecasting univariate time series with Autoregressive Integrated Moving Average (ARIMA) time series models. It sets up the model with a number of control variables that govern the way the rest of functions in the package will work. It also estimates the model parameters by Maximum Likelihood and forecasts the data.

Value

An object of class ARIMA. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ARIMA object as specified in what follows (function ARIMA fills in all of them at once):

After running ARIMAforecast or ARIMA:

After running ARIMAvalidate:

Author(s)

Diego J. Pedregal

See Also

ARIMA, ARIMAvalidate,

Examples

y <- log(AirPassengers)
m1 <- ARIMAforecast(y)
m1 <- ARIMAforecast(y, lambda = NULL)

ARIMAsetup

Description

Sets up ARIMA general models

Usage

ARIMAsetup(
  y,
  u = NULL,
  model = NULL,
  cnst = NULL,
  s = frequency(y),
  criterion = "bic",
  h = 2 * s,
  verbose = FALSE,
  lambda = 1,
  maxOrders = c(3, 2, 3, 2, 1, 2),
  bootstrap = FALSE,
  nSimul = 5000,
  fast = FALSE
)

Arguments

Details

See help of ARIMAforecast.

Value

An object of class ARIMA. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ARIMA object as specified in what follows (function ARIMA fills in all of them at once):

After running ARIMAforecast or ARIMA:

After running ARIMAvalidate:

Author(s)

Diego J. Pedregal

See Also

ARIMA, ARIMAforecast, ARIMAvalidate,

Examples

y <- log(AirPassengers)
m1 <- ARIMAsetup(y)
m1 <- ARIMAsetup(y, lambda = NULL)

ARIMAvalidate

Description

Shows a table of estimation and diagnostics results for ARIMA models

Usage

ARIMAvalidate(m)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

ARIMA, ARIMAforecast, ARIMAvalidate,

Examples

m1 <- ARIMAforecast(log(gdp))
m1 <- ARIMAvalidate(m1)

Accuracy

Description

Accuracy for 1 time series y and several forecasting methods py and h steps ahead py is h x nMethods x nSeries

Usage

Accuracy(py, y, s = frequency(y), collectFun = mean)

Arguments

Value

Table of accuracy results

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, tsDisplay, size

Examples

## Not run: Accuracy(py, y, 12)

BIC.UComp

Description

Extract BIC (or SBC) value of UComp object

Usage

## S3 method for class 'UComp'
BIC(object, ...)

Arguments

Details

Selection criteria for models with different number of parameters, the smaller BIC the better. The formula used here is BIC=(-2 ln(L) + k ln(n)) / n, where ln(L) is the log-likelihood at the optimum, k is the number of parameters plus non-stationary states and n is the number of observations. Mind that this formulation differs from the usual definition that does not divide by n. This makes that BIC(m) and BIC(logLik(m)) give different results, being m an UComp object.

Value

BIC value of a UC model

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents

Examples

y <- log(AirPassengers)
m1 <- UCforecast(y, model = "llt/equal/arma(0,0)")
BIC(m1)

ETS

Description

Runs all relevant functions for ETS modelling

Usage

ETS(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = 2 * s,
  criterion = "aicc",
  lambda = 1,
  armaIdent = FALSE,
  identAll = FALSE,
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(1e-08, 1 - 1e-08),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999
)

Arguments

Details

See help of ETSforecast.

Value

An object of class ETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ETS object as specified in what follows (function ETS fills in all of them at once):

After running ETSforecast:

After running ETSvalidate:

After running ETScomponents:

An object of class ETS. See ETSforecast.

Author(s)

Diego J. Pedregal

See Also

ETSforecast, ETSvalidate, ETScomponents

Examples

y <- log(AirPassengers)
m1 <- ETS(y)
m1 <- ETS(y, model = "MAM")

ETScomponents

Description

Estimates components of ETS models

Usage

ETScomponents(m)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

ETS, ETSforecast, ETSvalidate

Examples

m1 <- ETS(log(gdp))
m1 <- ETScomponents(m1)

ETSestim

Description

Estimates and forecasts ETS models

Usage

ETSestim(m)

Arguments

Details

ETSestim estimates and forecasts a time series using an an ETS model

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

ETS, ETSforecast, ETSvalidate, ETScomponents

Examples

m1 <- ETSsetup(log(gdp))
m1 <- ETSestim(m1)

ETSforecast

Description

Estimates and forecasts ETS general univariate models

Usage

ETSforecast(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = max(2 * s, 6),
  criterion = "aicc",
  lambda = 1,
  armaIdent = FALSE,
  identAll = FALSE,
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(1e-08, 1 - 1e-08),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999
)

Arguments

Details

ETSforecast is a function for modelling and forecasting univariate time series with ExponenTial Smoothing (ETS) time series models. It sets up the model with a number of control variables that govern the way the rest of functions in the package will work. It also estimates the model parameters by Maximum Likelihood and forecasts the data.

Value

An object of class ETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ETS object as specified in what follows (function ETS fills in all of them at once):

After running ETSforecast:

After running ETSvalidate:

After running ETScomponents:

Author(s)

Diego J. Pedregal

See Also

ETS, ETSvalidate, ETScomponents

Examples

y <- log(AirPassengers)
m1 <- ETSforecast(y)
m1 <- ETSforecast(y, model = "A?A")

ETSsetup

Description

Sets up ETS general univariate models

Usage

ETSsetup(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = 2 * s,
  criterion = "aicc",
  lambda = 1,
  armaIdent = FALSE,
  identAll = FALSE,
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(1e-08, 1 - 1e-08),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999
)

Arguments

Details

See help of ETSforecast.

Value

An object of class ETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any ETS object as specified in what follows (function ETS fills in all of them at once):

After running ETSforecast:

After running ETSvalidate:

After running ETScomponents:

An object of class ETS. See ETSforecast.

Author(s)

Diego J. Pedregal

See Also

ETS, ETSforecast, ETSvalidate, ETScomponents

Examples

y <- log(AirPassengers)
m1 <- ETSsetup(y)
m1 <- ETSsetup(y,  model = "???")
m1 <- ETSsetup(y,  model = "?AA")

ETSvalidate

Description

Shows a table of estimation and diagnostics results for ETS models

Usage

ETSvalidate(m)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

ETS, ETSforecast, ETSvalidate, ETScomponents

Examples

m1 <- ETSforecast(log(gdp))
m1 <- ETSvalidate(m1)

OECD GDP

Description

Seasonally adjusted quarterly OECD real gross domestic product (OECDgdp).

Usage

OECDgdp

Format

Time series objects.

Quarterly data from 1962 to 2019

https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/bdsice.aspx

Value

No return value, called for side effects

Examples

 OECDgdp

TETS

Description

Runs all relevant functions for TETS modelling

Usage

TETS(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = 2 * s,
  criterion = "aicc",
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(0, 1),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999,
  Ymin = -Inf,
  Ymax = Inf
)

Arguments

Details

See help of TETSforecast.

Value

An object of class TETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any TETS object as specified in what follows (function TETS fills in all of them at once):

After running TETSforecast:

After running TETSvalidate:

After running TETScomponents:

Author(s)

Diego J. Pedregal

See Also

TETSforecast, TETSvalidate, TETScomponents

Examples

y <- log(AirPassengers)
m1 <- TETS(y)
m1 <- TETS(y, model = "MAM")

TETScomponents

Description

Estimates components of TOBIT TETS models

Usage

TETScomponents(m)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

TETS, TETSforecast, TETSvalidate

Examples

m1 <- TETS(log(gdp))
m1 <- TETScomponents(m1)

TETSestim

Description

Estimates and forecasts TOBIT TETS models

Usage

TETSestim(m)

Arguments

Details

TETSestim estimates and forecasts a time series using an a TOBIT TETS model

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

TETS, TETSforecast, TETSvalidate, TETScomponents

Examples

m1 <- TETSsetup(log(gdp))
m1 <- TETSestim(m1)

TETSforecast

Description

Estimates and forecasts TOBIT TETS general univariate models

Usage

TETSforecast(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = max(2 * s, 6),
  criterion = "aicc",
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(0, 1),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999,
  Ymin = -Inf,
  Ymax = Inf
)

Arguments

Details

TETSforecast is a function for modelling and forecasting univariate time series with TOBIT ExponenTial Smoothing (TETS) time series models. It sets up the model with a number of control variables that govern the way the rest of functions in the package will work. It also estimates the model parameters by Maximum Likelihood and forecasts the data.

Value

An object of class TETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any TETS object as specified in what follows (function TETS fills in all of them at once):

After running TETSforecast:

After running TETSvalidate:

After running TETScomponents:

Author(s)

Diego J. Pedregal

See Also

TETS, TETSvalidate, TETScomponents

Examples

y <- log(AirPassengers)
m1 <- TETSforecast(y)
m1 <- TETSforecast(y, model = "A?A")

TETSsetup

Description

Sets up TOBIT TETS general univariate models

Usage

TETSsetup(
  y,
  u = NULL,
  model = "???",
  s = frequency(y),
  h = 2 * s,
  criterion = "aicc",
  forIntervals = FALSE,
  bootstrap = FALSE,
  nSimul = 5000,
  verbose = FALSE,
  alphaL = c(0, 1),
  betaL = alphaL,
  gammaL = alphaL,
  phiL = c(0.8, 0.98),
  p0 = -99999,
  Ymin = -Inf,
  Ymax = Inf
)

Arguments

Details

See help of TETSforecast.

Value

An object of class TETS. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any TETS object as specified in what follows (function TETS fills in all of them at once):

After running TETSforecast:

After running TETSvalidate:

After running TETScomponents:

Author(s)

Diego J. Pedregal

See Also

TETS, TETSforecast, TETSvalidate, TETScomponents

Examples

y <- log(AirPassengers)
m1 <- TETSsetup(y)
m1 <- TETSsetup(y,  model = "???")
m1 <- TETSsetup(y,  model = "?AA")

TETSvalidate

Description

Shows a table of estimation and diagnostics results for TOBIT TETS models

Usage

TETSvalidate(m)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

Author(s)

Diego J. Pedregal

See Also

TETS, TETSforecast, TETSvalidate, TETScomponents

Examples

m1 <- TETSforecast(log(gdp))
m1 <- TETSvalidate(m1)

UC

Description

Runs all relevant functions for UC modelling

Usage

UC(
  y,
  u = NULL,
  model = "?/none/?/?",
  h = 24,
  lambda = 1,
  outlier = 9999,
  tTest = FALSE,
  criterion = "aic",
  periods = NA,
  verbose = FALSE,
  stepwise = FALSE,
  p0 = -9999.9,
  arma = FALSE,
  TVP = NULL,
  trendOptions = "none/rw/llt/dt",
  seasonalOptions = "none/equal/different",
  irregularOptions = "none/arma(0,0)"
)

Arguments

Details

UC is a function for modelling and forecasting univariate time series according to Unobserved Components models (UC). It sets up the model with a number of control variables that govern the way the rest of functions in the package work. It also estimates the model parameters by Maximum Likelihood, forecasts the data, performs smoothing, estimates model disturbances, estimates components and shows statistical diagnostics. Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Value

An object of class UComp. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any UComp object as specified in what follows (function UC fills in all of them at once):

After running UCforecast or UCestim:

• p: Estimated parameters

• v: Estimated innovations (white noise in correctly specified models)

• yFor: Forecasted values of output

• yForV: Forecasted values +- one standard error

• criteria: Value of criteria for estimated model

• iter: Number of iterations in estimation

• grad: Gradient at estimated parameters

• covp: Covariance matrix of parameters

After running UCvalidate:

• table: Estimation and validation table

After running UCcomponents:

• comp: Estimated components in matrix form

• compV: Estimated components variance in matrix form

After running UCfilter, UCsmooth or UCdisturb:

• yFit: Fitted values of output

• yFitV: Variance of fitted values of output

• a: State estimates

• P: Variance of state estimates

• aFor: Forecasts of states

• PFor: Forecasts of states variances

After running UCdisturb:

• eta: State perturbations estimates

• eps: Observed perturbations estimates

Author(s)

Diego J. Pedregal

See Also

UC, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

y <- log(AirPassengers)
m1 <- UC(y)
m1 <- UC(y, model = "llt/different/arma(0,0)")

UCcommand

Description

Auxiliar function for UC modeling

Usage

UCcommand(command, sys)

Arguments

Value

The input UComp object with the appropriate fields filled in

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCcomponents, UCdisturb

Examples

cycle <- UChp(USgdp)
plot(cycle)

UCcomponents

Description

Estimates unobserved components of UC models Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Usage

UCcomponents(sys)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

• comp: Estimated components in matrix form

• compV: Estimated components variance in matrix form

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UChp

Examples

m1 <- UC(log(AirPassengers))
m1 <- UCcomponents(m1)

UCdisturb

Description

Runs the Disturbance Smoother for UC models Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Usage

UCdisturb(sys)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

• yFit: Fitted values of output

• yFitV: Variance of fitted values of output

• a: State estimates

• P: Variance of state estimates (diagonal of covariance matrices)

• eta: State perturbations estimates

• eps: Observed perturbations estimates

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCcomponents, UChp

Examples

m1 <- UC(log(AirPassengers))
m1 <- UCdisturb(m1)

UCestim

Description

Estimates and forecasts UC models

Usage

UCestim(sys)

Arguments

Details

UCestim estimates and forecasts a time series using an UC model. The optimization method is a BFGS quasi-Newton algorithm with a backtracking line search using Armijo conditions. Parameter names in output table are the following:

• Damping: Damping factor for DT trend.

• Level: Variance of level disturbance.

• Slope: Variance of slope disturbance.

• Rho(#): Damping factor of cycle #.

• Period(#): Estimated period of cycle #.

• Var(#): Variance of cycle #.

• Seas(#): Seasonal harmonic with period #.

• Irregular: Variance of irregular component.

• AR(#): AR parameter of lag #.

• MA(#): MA parameter of lag #.

• AO#: Additive outlier in observation #.

• LS#: Level shift outlier in observation #.

• SC#: Slope change outlier in observation #.

• Beta(#): Beta parameter of input #.

• Cnst: Constant.

Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Value

The same input object with the appropriate fields filled in, in particular:

• p: Estimated transformed parameters

• v: Estimated innovations (white noise in correctly specified models)

• yFor: Forecast values of output

• yForV: Forecasted values variance

• criteria: Value of criteria for estimated model

• covp: Covariance matrix of estimated transformed parameters

• grad: Gradient of log-likelihood at the optimum

• iter: Estimation iterations

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

m1 <- UCsetup(log(AirPassengers))
m1 <- UCestim(m1)

UCfilter

Description

Runs the Kalman Filter for UC models Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Usage

UCfilter(sys)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

• yFit: Fitted values of output

• yFitV: Variance of fitted values of output

• a: State estimates

• P: Variance of state estimates (diagonal of covariance matrices)

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

m1 <- UC(log(AirPassengers))
m1 <- UCfilter(m1)

UCforecast

Description

Estimates and forecasts UC general univariate models

Usage

UCforecast(
  y,
  u = NULL,
  model = "?/none/?/?",
  h = 24,
  lambda = 1,
  outlier = 9999,
  tTest = FALSE,
  criterion = "aic",
  periods = NA,
  verbose = FALSE,
  stepwise = FALSE,
  p0 = -9999.9,
  arma = FALSE,
  TVP = NULL,
  trendOptions = "none/rw/llt/dt",
  seasonalOptions = "none/equal/different",
  irregularOptions = "none/arma(0,0)"
)

Arguments

Details

UCforecast is a function for modelling and forecasting univariate time series according to Unobserved Components models (UC). It sets up the model with a number of control variables that govern the way the rest of functions in the package work. It also estimates the model parameters by Maximum Likelihood and forecasts the data. Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Value

An object of class UComp. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any UComp object as specified in what follows (function UC fills in all of them at once):

After running UCforecast:

• p: Estimated parameters

• v: Estimated innovations (white noise in correctly specified models)

• yFor: Forecasted values of output

• yForV: Forecasted values +- one standard error

• criteria: Value of criteria for estimated model

• iter: Number of iterations in estimation

• grad: Gradient at estimated parameters

• covp: Covariance matrix of parameters

After running UCvalidate:

• table: Estimation and validation table

After running UCcomponents:

• comp: Estimated components in matrix form

• compV: Estimated components variance in matrix form

After running UCfilter, UCsmooth or UCdisturb:

• yFit: Fitted values of output

• yFitV: Variance of fitted values of output

• a: State estimates

• P: Variance of state estimates

• aFor: Forecasts of states

• PFor: Forecasts of states variances

After running UCdisturb:

• eta: State perturbations estimates

• eps: Observed perturbations estimates

Author(s)

Diego J. Pedregal

See Also

UC, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

y <- log(AirPassengers)
m1 <- UCforecast(y)
m1 <- UCforecast(y, model = "llt/equal/arma(0,0)")

UChp

Description

Hodrick-Prescott filter estimation

Usage

UChp(y, lambda = 1600)

Arguments

Value

The cycle estimation

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCcomponents, UCdisturb

Examples

cycle <- UChp(USgdp)
plot(cycle)

UCsetup

Description

Sets up UC general univariate models

Usage

UCsetup(
  y,
  u = NULL,
  model = "?/none/?/?",
  h = 24,
  lambda = 1,
  outlier = 9999,
  tTest = FALSE,
  criterion = "aic",
  periods = NA,
  verbose = FALSE,
  stepwise = FALSE,
  p0 = -9999.9,
  arma = FALSE,
  TVP = NULL,
  trendOptions = "none/rw/llt/dt",
  seasonalOptions = "none/equal/different",
  irregularOptions = "none/arma(0,0)"
)

Arguments

Details

See help of UC.

Value

An object of class UComp. It is a list with fields including all the inputs and the fields listed below as outputs. All the functions in this package fill in part of the fields of any UComp object as specified in what follows (function UC fills in all of them at once):

After running UCforecast:

• p: Estimated parameters

• v: Estimated innovations (white noise in correctly specified models)

• yFor: Forecasted values of output

• yForV: Variance of forecasts

• criteria: Value of criteria for estimated model

• iter: Number of iterations in estimation

• grad: Gradient at estimated parameters

• covp: Covariance matrix of parameters

After running UCvalidate:

• table: Estimation and validation table

After running UCcomponents:

• comp: Estimated components in matrix form

• compV: Estimated components variance in matrix form

After running UCfilter, UCsmooth or UCdisturb:

• yFit: Fitted values of output

• yFitV: Estimated fitted values variance

• a: State estimates

• P: Variance of state estimates

• aFor: Forecasts of states

• PFor: Forecasts of states variances

After running UCdisturb:

• eta: State perturbations estimates

• eps: Observed perturbations estimates

Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

y <- log(AirPassengers)
m1 <- UCsetup(y)
m1 <- UCsetup(y, outlier = 4)
m1 <- UCsetup(y, model = "llt/equal/arma(0,0)")
m1 <- UCsetup(y, model = "?/?/?/?")
m1 <- UCsetup(y, model = "llt/?/equal/?", outlier = 4)

UCsmooth

Description

Runs the Fixed Interval Smoother for UC models Standard methods applicable to UComp objects are print, summary, plot, fitted, residuals, logLik, AIC, BIC, coef, predict, tsdiag.

Usage

UCsmooth(sys)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

• yFit: Fitted values of output

• yFitV: Variance of fitted values of output

• a: State estimates

• P: Variance of state estimates (diagonal of covariance matrices)

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCdisturb, UCcomponents, UChp

Examples

m1 <- UC(log(AirPassengers))
m1 <- UCsmooth(m1)

UCvalidate

Description

Shows a table of estimation and diagnostics results for UC models. Equivalent to print or summary. The table shows information in four sections: Firstly, information about the model estimated, the relevant periods of the seasonal component included, and further information about convergence. Secondly, parameters with their names are provided, the asymptotic standard errors, the ratio of the two, and the gradient at the optimum. One asterisk indicates concentrated-out parameters and two asterisks signals parameters constrained during estimation. Thirdly, information criteria and the value of the log-likelihood. Finally, diagnostic statistics about innovations, namely, the Ljung-Box Q test of absense of autocorrelation statistic for several lags, the Jarque-Bera gaussianity test, and a standard ratio of variances test.

Usage

UCvalidate(sys, printScreen = TRUE)

Arguments

Value

The same input object with the appropriate fields filled in, in particular:

• table: Estimation and validation table

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCfilter, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

m1 <- UC(log(gdp))
m1 <- UCvalidate(m1)

US GDP

Description

Seasonally adjusted quarterly US real gross domestic product (USgdp).

Usage

USgdp

Format

Time series objects.

Quarterly data from 1962 to 2019

Value

No return value, called for side effects

Source

USgdp

Examples

 USgdp

acft

Description

Theoretical autocorrelation functions of ARMA models

Usage

acft(MApoly = 1, ARpoly = 1, ncoef = 38, s = 1)

Arguments

Value

Theoretical autocorrelation functions

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, slide, plotSlide, Accuracy, tsDisplay, size

Examples

acft(c(1, -0.8), c(1, 0.8))

Airpassengers in Spain

Description

Foreign arrivals by air in Spain in thousands of passengers (airpas).

Usage

airpas

Format

Time series objects.

Monthly data from 1969

https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/bdsice.aspx

Value

No return value, called for side effects

Examples

 airpas

arma2tsi

Description

AR polynomial coefficients of ARMA model

Usage

arma2tsi(MApoly, ARpoly, n = 100)

Arguments

Value

Tsi (MA form) coefficients of equivalent ARMA model

Author(s)

Diego J. Pedregal

armaFilter

Description

Filter of time series

Usage

armaFilter(MA = 1, AR = 1, y)

Arguments

Value

Filtered time series

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

y <- armaFilter(1, c(1 , -0.8), rnorm(200))

auxInvBoxCox

Description

Inverse of Box-Cox transformation

Usage

auxInvBoxCox(y, lambda)

Arguments

Value

Inverse of Box-Cox heteroskedasticity transformation

Author(s)

Diego J. Pedregal

box.cox

Description

Runs Box-Cox transform of a time series

Usage

box.cox(x, lambda)

Arguments

Value

Box-Cox transformed time series

Author(s)

Diego J. Pedregal

See Also

inv.box.cox, UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents

Examples

y <- box.cox(AirPassengers, 0.5)
plot(y)

Methane concentration at Cape Grim in Australia

Description

Methane concentration at Cape Grim in Australia (ch4).

Usage

ch4

Format

Time series objects.

Monthly data from January 1992 to December 2019

Value

No return value, called for side effects

Source

CH4 data

Examples

 ch4

colMedians

Description

Medians of matrix by columns

Usage

colMedians(x, na.rm = TRUE, ...)

Arguments

Value

A vector with all the medians in columns

Author(s)

Diego J. Pedregal

See Also

rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

s <- colMedians(matrix(4, 3, 2))

conv

Description

1D convolution: filtering or polynomial multiplication

Usage

conv(...)

Arguments

Value

Convolution of all input vectors

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

conv(c(1, -1), c(1, -2, 1))
conv(c(1, -1), c(1, 0.8))

cusum

Description

Cusum and cusumsq tests

Usage

cusum(y, runFromTest = FALSE)

Arguments

Value

No return value, called for side effects

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

cusum(AirPassengers)

dif

Description

Discrete differencing of time series

Usage

dif(y, difs = 1, seas = 1)

Arguments

Value

Differenced time series

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

dif(AirPassengers)
dif(AirPassengers, 2)
dif(AirPassengers, c(1, 1), c(1, 12))

extract

Description

Reorder data frame returning column col reordered according to the values in column accordingTo

Usage

extract(x, col, accordingTo = 1)

Arguments

Value

Data frame reordered accoring to a given column data

Author(s)

Diego J. Pedregal

gaussTest

Description

Gaussianity tests

Usage

gaussTest(y, runFromTests = FALSE)

Arguments

Value

No return value, called for side effects

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

gaussTest(AirPassengers)

Spanish GDP

Description

Quarterly real Spanish Gross Domestic Product (gdp)

Usage

gdp

Format

Time series objects.

Quarterly since 1995

https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/bdsice.aspx

Value

No return value, called for side effects

Examples

 gdp

getp0

Description

Get initial conditions for parameters of UComp object

Usage

getp0(y, model = "llt/equal/arma(0,0)", periods = NA)

Arguments

Details

Provides initial parameters of a given model for the time series. They may be changed arbitrarily by the user to include as an input p0 to UC or UCforecast functions (see example below). There is no guarantee that the model will converge and selecting initial conditions should be used with care.

Value

A set of parameters p0 of an object of class UComp to use as input to UC, UCforecast.

Author(s)

Diego J. Pedregal

See Also

UC, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents, UChp

Examples

## Not run: 
p0 <- getp0(log(AirPassengers), model = "llt/equal/arma(0,0)")
p0[1] <- 0  # p0[1] <- NA
m <- UCforecast(log(AirPassengers), model = "llt/equal/arma(0,0)", p0 = p0)

## End(Not run)

ident

Description

Autocorrelation functions of a time series

Usage

ident(y, nCoef = min(37, floor(length(y)/4)), nPar = 0, runFromTests = FALSE)

Arguments

Value

A vector with output table including ACF, etc.

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

ident(AirPassengers)

inv.box.cox

Description

Runs inverse of Box-Cox transform of a time series

Usage

inv.box.cox(x, lambda)

Arguments

Value

Inverse Box-Cox transformed time series

Author(s)

Diego J. Pedregal

See Also

box.cox, UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents

Examples

y <- inv.box.cox(box.cox(AirPassengers, 0.5), 0.5)
plot(y)

invBoxCox

Description

Calculates inverse of Box-Cox transformation with confidence bands, calculated as const time the standard error

Usage

invBoxCox(y, yVar, lambda, const = 2)

Arguments

Value

Inverse of Box-Cox heteroskedasticity transformation with confidence bands

Author(s)

Diego J. Pedregal

Spanish Industrial Production Index

Description

Spanish Industrial Production Index (ipi).

Usage

ipi

Format

Objeto time series.

Monthly since 1975

https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/bdsice.aspx

Value

No return value, called for side effects

Examples

 ipi

plotAcfPacf

Description

Plot of ACF and PACF

Usage

plotAcfPacf(ACF, PACF, s = 1, n = NA, runFromTest = FALSE)

Arguments

Value

No return value, called for side effects

Author(s)

Diego J. Pedregal

plotBar

Description

Plot variable in bars

Usage

plotBar(ACF, s = 1, n = NA, label = "ACF")

Arguments

Value

Handle of plot

Author(s)

Diego J. Pedregal

plotSlide

Description

Plot summarised results from slide

Usage

plotSlide(py1, y, orig, step = 1, errorFun, collectFun = mean)

Arguments

Value

An array of forecasting errors of dimensions (horizon x nOrigs x nModels x nSeries)

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, Accuracy, tsDisplay, size

Examples

## Not run: plotSlide(py1, AirPassengers, 100, 1, errorFun)

plus_one

Description

Returns date of next to end time series y

Usage

plus_one(y)

Arguments

Value

Next time stamp

Author(s)

Diego J. Pedregal

predict.UComp

Description

Forecasting using structural Unobseved Components models with prediction intervals

Usage

## S3 method for class 'UComp'
predict(object, newdata = NULL, n.ahead = NULL, level = 0.95, ...)

Arguments

Details

See help of UC.

Value

Forecasts of a UC model

A matrix with the mean forecasts and lower and upper prediction intervals

Author(s)

Diego J. Pedregal

See Also

UC, UCforecast, UCvalidate, UCfilter, UCsmooth, UCdisturb, UCcomponents

Examples

y <- log(AirPassengers)
m1 <- UCforecast(y, model = "llt/eq/arma(0,0)")
f1 <- predict(m1)

removeNaNs

Description

Remove nans at beginning or end of vector

Usage

removeNaNs(x)

Arguments

Value

vector with nans removed (only those at beginning or end)

Author(s)

Diego J. Pedregal

roots

Description

Roots of polynomial

Usage

roots(x)

Arguments

Value

Roots of polynomial

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

roots(c(1, -2 ,1))
roots(conv(c(1, -1), c(1, 0.8)))

rowMedians

Description

Medians of matrix by rows

Usage

rowMedians(x, na.rm = TRUE, ...)

Arguments

Value

A vector with all the medians in rows

Author(s)

Diego J. Pedregal

See Also

colMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

s <- rowMedians(matrix(4, 3, 2))

Sales index for large retailers in Spain

Description

Sales index for food of large retailers in Spain

Usage

sales

Format

Time series objects.

Monthly data from January 1995 to December 2019

https://portal.mineco.gob.es/es-es/economiayempresa/EconomiaInformesMacro/Paginas/bdsice.aspx

Value

No return value, called for side effects

Examples

 sales

size

Description

Size of vector, matrix or array

Usage

size(y)

Arguments

Value

A vector with all the dimensions

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay

Examples

s <- size(matrix(4, 3, 2))
s <- size(rep(4, 3))
s <- size(array(4, c(3, 2, 2)))

slide

Description

Rolling forecasting of a matrix of time series

Usage

slide(
  y,
  orig,
  forecFun,
  ...,
  h = 12,
  step = 1,
  output = TRUE,
  window = NA,
  parallel = FALSE
)

Arguments

Details

Takes time series and run forecasting methods implemented in function forecFun h steps ahead along the time series y, starting at forecasting origin orig, and moving step observations ahead. Forecasts may be run in parallel by setting parallel to TRUE. A fixed window width may be specified with input window. The output is of dimensions (h, nOrigs, nModels, nSeries)

Value

An array of forecasts of dimensions (horizon x nOrigs x nModels x nSeries)

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, plotSlide, Accuracy, tsDisplay, size

Examples

## Not run: slide(AirPassengers, 100, forecFun)

slideAux

Description

Auxiliary function run from slide

Usage

slideAux(
  y,
  orig,
  forecFun,
  h = 12,
  step = 1,
  output = TRUE,
  graph = TRUE,
  window = NA,
  parallel = FALSE,
  isList = FALSE,
  ...
)

Arguments

Value

Auxiliary output of slide function for just one time series

Author(s)

Diego J. Pedregal

sumStats

Description

Summary statistics of a matrix of variables

Usage

sumStats(y, decimals = 5)

Arguments

Details

Position, dispersion, skewness, kurtosis, etc.

Value

Table of values in string matrix

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

s <- sumStats(AirPassengers)

tests

Description

Tests on a time series

Usage

tests(
  y,
  parts = 1/3,
  nCoef = min(25, length(x)/4),
  nPar = 0,
  s = frequency(y),
  avoid = 16
)

Arguments

Details

Multiple tests on a time series, including summary statistics, autocorrelation, Gaussianity and heteroskedasticity,

Value

Table with all test results

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

tests(AirPassengers)

tsDisplay

Description

Displays time series plot with autocorrelation functions

Usage

tsDisplay(y, nCoef = 25, nPar = 0, s = NA)

Arguments

Value

No return value, called for side effects

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, size

Examples

tsDisplay(AirPassengers)

varTest

Description

Ratio of variances test

Usage

varTest(y, parts = 1/3)

Arguments

Value

Table with test results

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, conv, armaFilter, dif, roots, zplane, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

varTest(AirPassengers)

zplane

Description

Real-imaginary plane to show roots of digital filters (ARMA)

Usage

zplane(MApoly = 1, ARpoly = 1)

Arguments

Details

Shows the real-imaginary plane to show zeros (roots of numerator or MA polynomial) and poles (roots of denominator of AR polynomial). Unit roots and real vs imaginary roots can be seen by eye

Value

No return value, called for side effects

Author(s)

Diego J. Pedregal

See Also

colMedians, rowMedians, tests, sumStats, gaussTest, ident, cusum, varTest, conv, armaFilter, dif, roots, acft, slide, plotSlide, Accuracy, tsDisplay, size

Examples

zplane(c(1, -2, 1), c(1, -0.8))