Package 'fable'

Description

Searches through the vector of lag orders to find the best AR model which has lowest AIC, AICc or BIC value. It is implemented using OLS, and behaves comparably to stats::ar.ols().

Usage

AR(formula, ic = c("aicc", "aic", "bic"), ...)

Arguments

Details

Exogenous regressors and common_xregs can be specified in the model formula.

Value

A model specification.

Specials

pdq

The order special is used to specify the lag order for the auto-regression.

order(p = 0:15, fixed = list())

xreg

Exogenous regressors can be included in an AR model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

The inclusion of a constant in the model follows the similar rules to stats::lm(), where including 1 will add a constant and 0 or -1 will remove the constant. If left out, the inclusion of a constant will be determined by minimising ic.

xreg(..., fixed = list())

See Also

Forecasting: Principles and Practices, Vector autoregressions (section 11.2)

Examples

luteinizing_hormones <- as_tsibble(lh)
fit <- luteinizing_hormones %>%
  model(AR(value ~ order(3)))

report(fit)

fit %>%
  forecast() %>%
  autoplot(luteinizing_hormones)

Description

Searches through the model space specified in the specials to identify the best ARIMA model, with the lowest AIC, AICc or BIC value. It is implemented using stats::arima() and allows ARIMA models to be used in the fable framework.

Usage

ARIMA(
  formula,
  ic = c("aicc", "aic", "bic"),
  selection_metric = function(x) x[[ic]],
  stepwise = TRUE,
  greedy = TRUE,
  approximation = NULL,
  order_constraint = p + q + P + Q <= 6 & (constant + d + D <= 2),
  unitroot_spec = unitroot_options(),
  trace = FALSE,
  ...
)

Arguments

Value

A model specification.

Parameterisation

The fable ARIMA() function uses an alternative parameterisation of constants to stats::arima() and forecast::Arima(). While the parameterisations are equivalent, the coefficients for the constant/mean will differ.

In fable, if there are no exogenous regressors, the parameterisation used is:

$(1-\phi_1B - \cdots - \phi_p B^p)(1-B)^d y_t = c + (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t$

In stats and forecast, an ARIMA model is parameterised as:

$(1-\phi_1B - \cdots - \phi_p B^p)(y_t' - \mu) = (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t$

where $\mu$ is the mean of $(1-B)^d y_t$ and $c = \mu(1-\phi_1 - \cdots - \phi_p )$.

If there are exogenous regressors, fable uses the same parameterisation as used in stats and forecast. That is, it fits a regression with ARIMA(p,d,q) errors:

$y_t = c + \beta' x_t + z_t$

where $\beta$ is a vector of regression coefficients, $x_t$ is a vector of exogenous regressors at time $t$, and $z_t$ is an ARIMA(p,d,q) error process:

$(1-\phi_1B - \cdots - \phi_p B^p)(1-B)^d z_t = (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t$

For details of the estimation algorithm, see the arima function in the stats package.

Specials

The specials define the space over which ARIMA will search for the model that best fits the data. If the RHS of formula is left blank, the default search space is given by pdq() + PDQ(): that is, a model with candidate seasonal and nonseasonal terms, but no exogenous regressors. Note that a seasonal model requires at least 2 full seasons of data; if this is not available, ARIMA will revert to a nonseasonal model with a warning.

To specify a model fully (avoid automatic selection), the intercept and pdq()/PDQ() values must be specified. For example, formula = response ~ 1 + pdq(1, 1, 1) + PDQ(1, 0, 0).

pdq

The pdq special is used to specify non-seasonal components of the model.

pdq(p = 0:5, d = 0:2, q = 0:5,
    p_init = 2, q_init = 2, fixed = list())

PDQ

The PDQ special is used to specify seasonal components of the model. To force a non-seasonal fit, specify PDQ(0, 0, 0) in the RHS of the model formula. Note that simply omitting PDQ from the formula will not result in a non-seasonal fit.

PDQ(P = 0:2, D = 0:1, Q = 0:2, period = NULL,
    P_init = 1, Q_init = 1, fixed = list())

xreg

Exogenous regressors can be included in an ARIMA model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

The inclusion of a constant in the model follows the similar rules to stats::lm(), where including 1 will add a constant and 0 or -1 will remove the constant. If left out, the inclusion of a constant will be determined by minimising ic.

xreg(..., fixed = list())

See Also

Forecasting: Principles and Practices, ARIMA models (chapter 9) Forecasting: Principles and Practices, Dynamic regression models (chapter 10)

Examples

# Manual ARIMA specification
USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ 0 + pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  report()

# Automatic ARIMA specification
library(tsibble)
library(dplyr)
tsibbledata::global_economy %>%
  filter(Country == "Australia") %>%
  model(ARIMA(log(GDP) ~ Population))

Description

Breusch-Godfrey test for higher-order serial correlation.

Usage

breusch_godfrey(x, ...)

## S3 method for class 'TSLM'
breusch_godfrey(x, order = 1, type = c("Chisq", "F"), ...)

Arguments

See Also

lmtest::bgtest()

Description

Extract estimated states from an ETS model.

Usage

## S3 method for class 'ETS'
components(object, ...)

Arguments

Value

A fabletools::dable() containing estimated states.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  components()

Description

Based on Croston's (1972) method for intermittent demand forecasting, also described in Shenstone and Hyndman (2005). Croston's method involves using simple exponential smoothing (SES) on the non-zero elements of the time series and a separate application of SES to the times between non-zero elements of the time series.

Usage

CROSTON(
  formula,
  opt_crit = c("mse", "mae"),
  type = c("croston", "sba", "sbj"),
  ...
)

Arguments

Details

Note that forecast distributions are not computed as Croston's method has no underlying stochastic model. In a later update, we plan to support distributions via the equivalent stochastic models that underly Croston's method (Shenstone and Hyndman, 2005)

There are two variant methods available which apply multiplicative correction factors to the forecasts that result from the original Croston's method. For the Syntetos-Boylan approximation (type = "sba"), this factor is $1 - \alpha / 2$, and for the Shale-Boylan-Johnston method (type = "sbj"), this factor is $1 - \alpha / (2 - \alpha)$, where $\alpha$ is the smoothing parameter for the interval SES application.

Value

A model specification.

Specials

demand

The demand special specifies parameters for the demand SES application.

demand(initial = NULL, param = NULL, param_range = c(0, 1))

interval

The interval special specifies parameters for the interval SES application.

interval(initial = NULL, param = NULL, param_range = c(0, 1))

References

Croston, J. (1972) "Forecasting and stock control for intermittent demands", Operational Research Quarterly, 23(3), 289-303.

Shenstone, L., and Hyndman, R.J. (2005) "Stochastic models underlying Croston's method for intermittent demand forecasting". Journal of Forecasting, 24, 389-402.

Kourentzes, N. (2014) "On intermittent demand model optimisation and selection". International Journal of Production Economics, 156, 180-190. doi:10.1016/j.ijpe.2014.06.007.

Examples

library(tsibble)
sim_poisson <- tsibble(
  time = yearmonth("2012 Dec") + seq_len(24),
  count = rpois(24, lambda = 0.3),
  index = time
)

sim_poisson %>%
  autoplot(count)

sim_poisson %>%
  model(CROSTON(count)) %>%
  forecast(h = "2 years") %>%
  autoplot(sim_poisson)

Description

Returns ETS model specified by the formula.

Usage

ETS(
  formula,
  opt_crit = c("lik", "amse", "mse", "sigma", "mae"),
  nmse = 3,
  bounds = c("both", "usual", "admissible"),
  ic = c("aicc", "aic", "bic"),
  restrict = TRUE,
  ...
)

Arguments

Details

Based on the classification of methods as described in Hyndman et al (2008).

The methodology is fully automatic. The model is chosen automatically if not specified. This methodology performed extremely well on the M3-competition data. (See Hyndman, et al, 2002, below.)

Value

A model specification.

Specials

The specials define the methods and parameters for the components (error, trend, and seasonality) of an ETS model. If more than one method is specified, ETS will consider all combinations of the specified models and select the model which best fits the data (minimising ic). The method argument for each specials have reasonable defaults, so if a component is not specified an appropriate method will be chosen automatically.

There are a couple of limitations to note about ETS models:

• It does not support exogenous regressors.

• It does not support missing values. You can complete missing values in the data with imputed values (e.g. with tidyr::fill(), or by fitting a different model type and then calling fabletools::interpolate()) before fitting the model.

error

The error special is used to specify the form of the error term.

error(method = c("A", "M"))

trend

The trend special is used to specify the form of the trend term and associated parameters.

trend(method = c("N", "A", "Ad"),
      alpha = NULL, alpha_range = c(1e-04, 0.9999),
      beta = NULL, beta_range = c(1e-04, 0.9999),
      phi = NULL, phi_range = c(0.8, 0.98))

season

The season special is used to specify the form of the seasonal term and associated parameters. To specify a nonseasonal model you would include season(method = "N").

season(method = c("N", "A", "M"), period = NULL,
       gamma = NULL, gamma_range = c(1e-04, 0.9999))

References

Hyndman, R.J., Koehler, A.B., Snyder, R.D., and Grose, S. (2002) "A state space framework for automatic forecasting using exponential smoothing methods", International J. Forecasting, 18(3), 439–454.

Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible parameter space for exponential smoothing models". Annals of Statistical Mathematics, 60(2), 407–426.

Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag. http://www.exponentialsmoothing.net.

See Also

Forecasting: Principles and Practices, Exponential smoothing (chapter 8)

Examples

as_tsibble(USAccDeaths) %>%
  model(ETS(log(value) ~ season("A")))

Description

Extracts the fitted values.

Usage

## S3 method for class 'AR'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

as_tsibble(lh) %>%
  model(AR(value ~ order(3))) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'ARIMA'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'croston'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

library(tsibble)
sim_poisson <- tsibble(
  time = yearmonth("2012 Dec") + seq_len(24),
  count = rpois(24, lambda = 0.3),
  index = time
)

sim_poisson %>%
  model(CROSTON(count)) %>%
  tidy()

Description

Extracts the fitted values.

Usage

## S3 method for class 'ETS'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'fable_theta'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'model_mean'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'NNETAR'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15))) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'RW'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  fitted()

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'TSLM'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  fitted()

Description

Extracts the fitted values.

Usage

## S3 method for class 'VAR'
fitted(object, ...)

Arguments

Value

A vector of fitted values.

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

lung_deaths %>%
  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) %>%
  fitted()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'AR'
forecast(
  object,
  new_data = NULL,
  specials = NULL,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

as_tsibble(lh) %>%
  model(AR(value ~ order(3))) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'ARIMA'
forecast(
  object,
  new_data = NULL,
  specials = NULL,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'croston'
forecast(object, new_data, specials = NULL, ...)

Arguments

Value

A list of forecasts.

Examples

library(tsibble)
sim_poisson <- tsibble(
  time = yearmonth("2012 Dec") + seq_len(24),
  count = rpois(24, lambda = 0.3),
  index = time
)

sim_poisson %>%
  model(CROSTON(count)) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'ETS'
forecast(
  object,
  new_data,
  specials = NULL,
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'fable_theta'
forecast(
  object,
  new_data,
  specials = NULL,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'model_mean'
forecast(
  object,
  new_data,
  specials = NULL,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'NNETAR'
forecast(
  object,
  new_data,
  specials = NULL,
  simulate = TRUE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15))) %>%
  forecast(times = 10)

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'RW'
forecast(
  object,
  new_data,
  specials = NULL,
  simulate = FALSE,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  forecast()

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'TSLM'
forecast(
  object,
  new_data,
  specials = NULL,
  bootstrap = FALSE,
  approx_normal = TRUE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  forecast()

Description

Produces forecasts from a trained model.

Usage

## S3 method for class 'VAR'
forecast(
  object,
  new_data = NULL,
  specials = NULL,
  bootstrap = FALSE,
  times = 5000,
  ...
)

Arguments

Value

A list of forecasts.

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

lung_deaths %>%
  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) %>%
  forecast()

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'AR'
generate(x, new_data = NULL, specials = NULL, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(lh) %>%
  model(AR(value ~ order(3))) %>%
  generate()

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'ARIMA'
generate(x, new_data, specials, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

fable_fit <- as_tsibble(USAccDeaths) %>%
  model(model = ARIMA(value ~ 0 + pdq(0,1,1) + PDQ(0,1,1)))
fable_fit %>% generate(times = 10)

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'ETS'
generate(x, new_data, specials, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(USAccDeaths) %>%
  model(ETS(log(value) ~ season("A"))) %>%
  generate(times = 100)

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'model_mean'
generate(x, new_data, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  generate()

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'NNETAR'
generate(x, new_data, specials = NULL, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15))) %>%
  generate()

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'RW'
generate(x, new_data, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  generate()

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  generate()

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'TSLM'
generate(x, new_data, specials, bootstrap = FALSE, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  generate()

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'VAR'
generate(x, new_data, specials, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(USAccDeaths) %>%
  model(ETS(log(value) ~ season("A"))) %>%
  generate(times = 100)

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'VECM'
generate(x, new_data, specials, ...)

Arguments

See Also

fabletools::generate.mdl_df

Examples

as_tsibble(USAccDeaths) %>%
  model(ETS(log(value) ~ season("A"))) %>%
  generate(times = 100)

Description

Construct a single row summary of the AR model.

Usage

## S3 method for class 'AR'
glance(x, ...)

Arguments

Details

Contains the variance of residuals (sigma2), the log-likelihood (log_lik), and information criterion (AIC, AICc, BIC).

Value

A one row tibble summarising the model's fit.

Examples

as_tsibble(lh) %>%
  model(AR(value ~ order(3))) %>%
  glance()

Description

Construct a single row summary of the ARIMA model.

Usage

## S3 method for class 'ARIMA'
glance(x, ...)

Arguments

Format

A data frame with 1 row, with columns:

sigma2

The unbiased variance of residuals. Calculated as sum(residuals^2) / (num_observations - num_pararameters + 1)

log_lik

The log-likelihood

AIC

Akaike information criterion

AICc

Akaike information criterion, corrected for small sample sizes

BIC

Bayesian information criterion

ar_roots, ma_roots

The model's characteristic roots

Value

A one row tibble summarising the model's fit.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  glance()

Description

Construct a single row summary of the ETS model.

Usage

## S3 method for class 'ETS'
glance(x, ...)

Arguments

Details

Contains the variance of residuals (sigma2), the log-likelihood (log_lik), and information criterion (AIC, AICc, BIC).

Value

A one row tibble summarising the model's fit.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  glance()

Description

Construct a single row summary of the average method model.

Usage

## S3 method for class 'fable_theta'
glance(x, ...)

Arguments

Details

Contains the variance of residuals (sigma2).

Value

A one row tibble summarising the model's fit.

Description

Construct a single row summary of the average method model.

Usage

## S3 method for class 'model_mean'
glance(x, ...)

Arguments

Details

Contains the variance of residuals (sigma2).

Value

A one row tibble summarising the model's fit.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  glance()

Description

Construct a single row summary of the NNETAR model. Contains the variance of residuals (sigma2).

Usage

## S3 method for class 'NNETAR'
glance(x, ...)

Arguments

Value

A one row tibble summarising the model's fit.

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15))) %>%
  glance()

Description

Construct a single row summary of the lag walk model. Contains the variance of residuals (sigma2).

Usage

## S3 method for class 'RW'
glance(x, ...)

Arguments

Value

A one row tibble summarising the model's fit.

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  glance()

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  glance()

Description

Construct a single row summary of the TSLM model.

Usage

## S3 method for class 'TSLM'
glance(x, ...)

Arguments

Details

Contains the R squared (r_squared), variance of residuals (sigma2), the log-likelihood (log_lik), and information criterion (AIC, AICc, BIC).

Value

A one row tibble summarising the model's fit.

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  glance()

Description

Construct a single row summary of the VAR model.

Usage

## S3 method for class 'VAR'
glance(x, ...)

Arguments

Details

Contains the variance of residuals (sigma2), the log-likelihood (log_lik), and information criterion (AIC, AICc, BIC).

Value

A one row tibble summarising the model's fit.

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

lung_deaths %>%
  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) %>%
  glance()

Description

Construct a single row summary of the VECM model.

Usage

## S3 method for class 'VECM'
glance(x, ...)

Arguments

Details

Contains the variance of residuals (sigma2), the log-likelihood (log_lik), the cointegrating vector (beta) and information criterion (AIC, AICc, BIC).

Value

A one row tibble summarising the model's fit.

Description

Applies a model-specific estimation technique to predict the values of missing values in a tsibble, and replace them.

Usage

## S3 method for class 'ARIMA'
interpolate(object, new_data, specials, ...)

Arguments

Value

A tibble of the same dimension of new_data with missing values interpolated.

Examples

library(tsibbledata)

olympic_running %>%
  model(arima = ARIMA(Time ~ trend())) %>%
  interpolate(olympic_running)

Description

Applies a model-specific estimation technique to predict the values of missing values in a tsibble, and replace them.

Usage

## S3 method for class 'model_mean'
interpolate(object, new_data, specials, ...)

Arguments

Value

A tibble of the same dimension of new_data with missing values interpolated.

Examples

library(tsibbledata)

olympic_running %>%
  model(mean = MEAN(Time)) %>%
  interpolate(olympic_running)

Description

Applies a model-specific estimation technique to predict the values of missing values in a tsibble, and replace them.

Usage

## S3 method for class 'TSLM'
interpolate(object, new_data, specials, ...)

Arguments

Value

A tibble of the same dimension of new_data with missing values interpolated.

Examples

library(tsibbledata)

olympic_running %>%
  model(lm = TSLM(Time ~ trend())) %>%
  interpolate(olympic_running)

Description

Calculate impulse responses from a fable model

Usage

## S3 method for class 'ARIMA'
IRF(x, new_data, specials, ...)

Arguments

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'VAR'
IRF(x, new_data, specials, impulse = NULL, orthogonal = FALSE, ...)

Arguments

Description

Simulates future paths from a dataset using a fitted model. Innovations are sampled by the model's assumed error distribution. If bootstrap is TRUE, innovations will be sampled from the model's residuals. If new_data contains the .innov column, those values will be treated as innovations.

Usage

## S3 method for class 'VECM'
IRF(x, new_data, specials, impulse = NULL, orthogonal = FALSE, ...)

Arguments

Description

MEAN() returns an iid model applied to the formula's response variable.

Usage

MEAN(formula, ...)

Arguments

Value

A model specification.

Specials

window

The window special is used to specify a rolling window for the mean.

window(size = NULL)

See Also

Forecasting: Principles and Practices, Some simple forecasting methods (section 3.2)

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand))

Description

Feed-forward neural networks with a single hidden layer and lagged inputs for forecasting univariate time series.

Usage

NNETAR(formula, n_nodes = NULL, n_networks = 20, scale_inputs = TRUE, ...)

Arguments

Details

A feed-forward neural network is fitted with lagged values of the response as inputs and a single hidden layer with size nodes. The inputs are for lags 1 to p, and lags m to mP where m is the seasonal period specified.

If exogenous regressors are provided, its columns are also used as inputs. Missing values are currently not supported by this model. A total of repeats networks are fitted, each with random starting weights. These are then averaged when computing forecasts. The network is trained for one-step forecasting. Multi-step forecasts are computed recursively.

For non-seasonal data, the fitted model is denoted as an NNAR(p,k) model, where k is the number of hidden nodes. This is analogous to an AR(p) model but with non-linear functions. For seasonal data, the fitted model is called an NNAR(p,P,k)[m] model, which is analogous to an ARIMA(p,0,0)(P,0,0)[m] model but with non-linear functions.

Value

A model specification.

Specials

AR

The AR special is used to specify auto-regressive components in each of the nodes of the neural network.

AR(p = NULL, P = 1, period = NULL)

xreg

Exogenous regressors can be included in an NNETAR model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

xreg(...)

See Also

Forecasting: Principles and Practices, Neural network models (section 11.3)

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15)))

Description

Applies a fitted AR model to a new dataset.

Usage

## S3 method for class 'AR'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

Value

A refitted model.

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(AR(value ~ 1 + order(10)))

report(fit)

fit %>%
  refit(lung_deaths_female) %>%
  report()

Description

Applies a fitted ARIMA model to a new dataset.

Usage

## S3 method for class 'ARIMA'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

Value

A refitted model.

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(ARIMA(value ~ 1 + pdq(2, 0, 0) + PDQ(2, 1, 0)))

report(fit)

fit %>%
  refit(lung_deaths_female) %>%
  report()

Description

Applies a fitted ETS model to a new dataset.

Usage

## S3 method for class 'ETS'
refit(
  object,
  new_data,
  specials = NULL,
  reestimate = FALSE,
  reinitialise = TRUE,
  ...
)

Arguments

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(ETS(value))

report(fit)

fit %>%
  refit(lung_deaths_female, reinitialise = TRUE) %>%
  report()

Description

Applies a fitted average method model to a new dataset.

Usage

## S3 method for class 'model_mean'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(MEAN(value))

report(fit)

fit %>%
  refit(lung_deaths_female) %>%
  report()

Description

Applies a fitted NNETAR model to a new dataset.

Usage

## S3 method for class 'NNETAR'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

Value

A refitted model.

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(NNETAR(value))

report(fit)

fit %>%
  refit(new_data = lung_deaths_female, reestimate = FALSE) %>%
  report()

Description

Applies a fitted random walk model to a new dataset.

Usage

## S3 method for class 'RW'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

Details

The models NAIVE and SNAIVE have no specific model parameters. Using refit for one of these models will provide the same estimation results as one would use fabletools::model(NAIVE(...)) (or fabletools::model(SNAIVE(...)).

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(RW(value ~ drift()))

report(fit)

fit %>%
  refit(lung_deaths_female) %>%
  report()

Description

Applies a fitted TSLM to a new dataset.

Usage

## S3 method for class 'TSLM'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)

Arguments

Examples

lung_deaths_male <- as_tsibble(mdeaths)
lung_deaths_female <- as_tsibble(fdeaths)

fit <- lung_deaths_male %>%
  model(TSLM(value ~ trend() + season()))

report(fit)

fit %>%
  refit(lung_deaths_female) %>%
  report()

Description

Extracts the residuals.

Usage

## S3 method for class 'AR'
residuals(object, type = c("innovation", "regression"), ...)

Arguments

Value

A vector of fitted residuals.

Examples

as_tsibble(lh) %>%
  model(AR(value ~ order(3))) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'ARIMA'
residuals(object, type = c("innovation", "regression"), ...)

Arguments

Value

A vector of fitted residuals.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'croston'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

library(tsibble)
sim_poisson <- tsibble(
  time = yearmonth("2012 Dec") + seq_len(24),
  count = rpois(24, lambda = 0.3),
  index = time
)

sim_poisson %>%
  model(CROSTON(count)) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'ETS'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'fable_theta'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'model_mean'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'NNETAR'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15))) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'RW'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  residuals()

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'TSLM'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  residuals()

Description

Extracts the residuals.

Usage

## S3 method for class 'VAR'
residuals(object, ...)

Arguments

Value

A vector of fitted residuals.

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

lung_deaths %>%
  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) %>%
  residuals()

Description

RW() returns a random walk model, which is equivalent to an ARIMA(0,1,0) model with an optional drift coefficient included using drift(). naive() is simply a wrapper to rwf() for simplicity. snaive() returns forecasts and prediction intervals from an ARIMA(0,0,0)(0,1,0)m model where m is the seasonal period.

Usage

RW(formula, ...)

NAIVE(formula, ...)

SNAIVE(formula, ...)

Arguments

Details

The random walk with drift model is

$Y_t=c + Y_{t-1} + Z_t$

where $Z_t$ is a normal iid error. Forecasts are given by

$Y_n(h)=ch+Y_n$

. If there is no drift (as in naive), the drift parameter c=0. Forecast standard errors allow for uncertainty in estimating the drift parameter (unlike the corresponding forecasts obtained by fitting an ARIMA model directly).

The seasonal naive model is

$Y_t= Y_{t-m} + Z_t$

where $Z_t$ is a normal iid error.

Value

A model specification.

Specials

lag

The lag special is used to specify the lag order for the random walk process. If left out, this special will automatically be included.

lag(lag = NULL)

drift

The drift special can be used to include a drift/trend component into the model. By default, drift is not included unless drift() is included in the formula.

drift(drift = TRUE)

See Also

Forecasting: Principles and Practices, Some simple forecasting methods (section 3.2)

Examples

library(tsibbledata)
aus_production %>%
  model(rw = RW(Beer ~ drift()))

as_tsibble(Nile) %>%
  model(NAIVE(value))
library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year")))

Description

The theta method of Assimakopoulos and Nikolopoulos (2000) is equivalent to simple exponential smoothing with drift. This is demonstrated in Hyndman and Billah (2003).

Usage

THETA(formula, ...)

Arguments

Details

The series is tested for seasonality using the test outlined in A&N. If deemed seasonal, the series is seasonally adjusted using a classical multiplicative decomposition before applying the theta method. The resulting forecasts are then reseasonalized.

More general theta methods are available in the forecTheta package.

Value

A model specification.

Specials

season

The season special is used to specify the parameters of the seasonal adjustment via classical decomposition.

season(period = NULL, method = c("multiplicative", "additive"))

Author(s)

Rob J Hyndman, Mitchell O'Hara-Wild

References

Assimakopoulos, V. and Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting 16, 521-530.

Hyndman, R.J., and Billah, B. (2003) Unmasking the Theta method. International J. Forecasting, 19, 287-290.

Examples

# Theta method with transform
deaths <- as_tsibble(USAccDeaths)
deaths %>%
  model(theta = THETA(log(value))) %>%
  forecast(h = "4 years") %>%
  autoplot(deaths)

# Compare seasonal specifications
library(tsibbledata)
library(dplyr)
aus_retail %>%
  filter(Industry == "Clothing retailing") %>%
  model(theta_multiplicative = THETA(Turnover ~ season(method = "multiplicative")),
        theta_additive = THETA(Turnover ~ season(method = "additive"))) %>%
  accuracy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'AR'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

as_tsibble(lh) %>%
  model(AR(value ~ order(3))) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'ARIMA'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'croston'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

library(tsibble)
sim_poisson <- tsibble(
  time = yearmonth("2012 Dec") + seq_len(24),
  count = rpois(24, lambda = 0.3),
  index = time
)

sim_poisson %>%
  model(CROSTON(count)) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'ETS'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

as_tsibble(USAccDeaths) %>%
  model(ets = ETS(log(value) ~ season("A"))) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'fable_theta'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

USAccDeaths %>%
  as_tsibble() %>%
  model(arima = ARIMA(log(value) ~ pdq(0, 1, 1) + PDQ(0, 1, 1))) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'model_mean'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

library(tsibbledata)
vic_elec %>%
  model(avg = MEAN(Demand)) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'NNETAR'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

as_tsibble(airmiles) %>%
  model(nn = NNETAR(box_cox(value, 0.15))) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'RW'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

as_tsibble(Nile) %>%
  model(NAIVE(value)) %>%
  tidy()

library(tsibbledata)
aus_production %>%
  model(snaive = SNAIVE(Beer ~ lag("year"))) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'TSLM'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season())) %>%
  tidy()

Description

Returns the coefficients from the model in a tibble format.

Usage

## S3 method for class 'VAR'
tidy(x, ...)

Arguments

Value

The model's coefficients in a tibble.

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

lung_deaths %>%
  model(VAR(vars(mdeaths, fdeaths) ~ AR(3))) %>%
  tidy()

Description

The model formula will be handled using stats::model.matrix(), and so the the same approach to include interactions in stats::lm() applies when specifying the formula. In addition to stats::lm(), it is possible to include common_xregs in the model formula, such as trend(), season(), and fourier().

Usage

TSLM(formula)

Arguments

Value

A model specification.

Specials

xreg

Exogenous regressors can be included in a TSLM model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

xreg(...)

See Also

stats::lm(), stats::model.matrix() Forecasting: Principles and Practices, Time series regression models (chapter 6)

Examples

as_tsibble(USAccDeaths) %>%
  model(lm = TSLM(log(value) ~ trend() + season()))

library(tsibbledata)
olympic_running %>%
  model(TSLM(Time ~ trend())) %>%
  interpolate(olympic_running)

Description

By default, a kpss test (via feasts::unitroot_kpss()) will be performed for testing the required first order differences, and a test of the seasonal strength (via feasts::feat_stl() seasonal_strength) being above the 0.64 threshold is used for determining seasonal required differences.

Usage

unitroot_options(
  ndiffs_alpha = 0.05,
  nsdiffs_alpha = 0.05,
  ndiffs_pvalue = ~feasts::unitroot_kpss(.)["kpss_pvalue"],
  nsdiffs_pvalue = ur_seasonal_strength(0.64)
)

Arguments

Value

A list of parameters

Description

Searches through the vector of lag orders to find the best VAR model which has lowest AIC, AICc or BIC value. It is implemented using OLS per equation.

Usage

VAR(formula, ic = c("aicc", "aic", "bic"), ...)

Arguments

Details

Exogenous regressors and common_xregs can be specified in the model formula.

Value

A model specification.

Specials

AR

The AR special is used to specify the lag order for the auto-regression.

AR(p = 0:5)

xreg

Exogenous regressors can be included in an VAR model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

The inclusion of a constant in the model follows the similar rules to stats::lm(), where including 1 will add a constant and 0 or -1 will remove the constant. If left out, the inclusion of a constant will be determined by minimising ic.

xreg(...)

See Also

Forecasting: Principles and Practices, Vector autoregressions (section 11.2)

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

fit <- lung_deaths %>%
  model(VAR(vars(mdeaths, fdeaths) ~ AR(3)))

report(fit)

fit %>%
  forecast() %>%
  autoplot(lung_deaths)

Description

Estimates a VARIMA model of a given order.

Usage

VARIMA(formula, identification = c("kronecker_indices", "none"), ...)

## S3 method for class 'VARIMA'
forecast(
  object,
  new_data = NULL,
  specials = NULL,
  bootstrap = FALSE,
  times = 5000,
  ...
)

## S3 method for class 'VARIMA'
fitted(object, ...)

## S3 method for class 'VARIMA'
residuals(object, ...)

## S3 method for class 'VARIMA'
tidy(x, ...)

## S3 method for class 'VARIMA'
glance(x, ...)

## S3 method for class 'VARIMA'
report(object, ...)

## S3 method for class 'VARIMA'
generate(x, new_data, specials, ...)

## S3 method for class 'VARIMA'
IRF(x, new_data, specials, impulse = NULL, orthogonal = FALSE, ...)

Arguments

Details

Exogenous regressors and common_xregs can be specified in the model formula.

Value

A model specification.

A one row tibble summarising the model's fit.

Specials

pdq

The pdq special is used to specify non-seasonal components of the model.

pdq(p = 0:5, d = 0:2, q = 0:5)

xreg

Exogenous regressors can be included in an VARIMA model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

The inclusion of a constant in the model follows the similar rules to stats::lm(), where including 1 will add a constant and 0 or -1 will remove the constant. If left out, the inclusion of a constant will be determined by minimising ic.

xreg(...)

See Also

MTS::VARMA(), MTS::Kronfit().

Examples

library(tsibbledata)

aus_production %>% 
  autoplot(vars(Beer, Cement))

fit <- aus_production %>%
  model(VARIMA(vars(Beer, Cement) ~ pdq(4,1,1), identification = "none"))

fit



fit %>%
  forecast(h = 50) %>%
  autoplot(tail(aus_production, 100))



fitted(fit)


residuals(fit)


tidy(fit)


glance(fit)


report(fit)


generate(fit, h = 10)


IRF(fit, h = 10, impulse = "Beer")

Description

Searches through the vector of lag orders to find the best VECM model which has lowest AIC, AICc or BIC value. The model is estimated using the Johansen procedure (maximum likelihood).

Usage

VECM(formula, ic = c("aicc", "aic", "bic"), r = 1L, ...)

Arguments

Details

Exogenous regressors and common_xregs can be specified in the model formula.

Value

A model specification.

Specials

AR

The AR special is used to specify the lag order for the auto-regression.

AR(p = 0:5)

xreg

Exogenous regressors can be included in an VECM model without explicitly using the xreg() special. Common exogenous regressor specials as specified in common_xregs can also be used. These regressors are handled using stats::model.frame(), and so interactions and other functionality behaves similarly to stats::lm().

The inclusion of a constant in the model follows the similar rules to stats::lm(), where including 1 will add a constant and 0 or -1 will remove the constant. If left out, the inclusion of a constant will be determined by minimising ic.

xreg(...)

Examples

lung_deaths <- cbind(mdeaths, fdeaths) %>%
  as_tsibble(pivot_longer = FALSE)

fit <- lung_deaths %>%
  model(VECM(vars(mdeaths, fdeaths) ~ AR(3)))

report(fit)

fit %>%
  forecast() %>%
  autoplot(lung_deaths)