tslm: Generate Time Series Regression Model

Description

Fit Time Series Regression Models.

Usage

tslm(
  x,
  trend.order = 1,
  seasonal = FALSE,
  period = NA,
  type = c("additive", "multiplicative"),
  train.prop = 1
)
# S3 method for tslm
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for tslm
summary(
  object,
  anova = TRUE,
  digits = max(3L, getOption("digits") - 3L),
  se = TRUE,
  signif.stars = TRUE,
  ...
)

Value

An object of class `tslm` is a list containing at least the following components:

coefficients: a named vector of coefficients
residuals: the residuals, that is response minus fitted values
rank: the numeric rank of the fitted linear model
fitted.values: the fitted mean values
df.residual: the residual degrees of freedom
call: the matched call
terms: the terms object used
xlevels: (only where relevant) a record of the levels of the factors used in fitting
offset: the offset used (missing if none were used)
model: if requested (the default), the model frame used
train.prop: proportion of training data.
x: data of the original series.
x.time: list of time in which the series values were observed.
x.timegap: time gap between the series and forecasted values.
x.name: name of the time series for which forecasts was requested.
x.time.used: list of time in which the series values were used for model fitting. It will be the same as x.time if train.prop = 1.
x.used: data of the original series which were used for model fitting. It will be the same as x if train.prop = 1.
series: series name x in match call.
error: a list of prediction error estimators, including $ME for mean error, $RMSE for root mean squared error, $MAE for mean absolute error, $MPE for mean percentage error, $MAPE for mean absolute percentage error, $MASE for mean absolute scaled error, $MASE.S for seasonal mean absolute scaled error, and $ACF1 for lag 1 autocorrelation.
model.test: a list of information regarding the prediction of the testing data including `x.test` (part of `x` used for testing), `fitted.test` (predicted values of the testing data), `residuals.test` (prediction error of the testing data), and `error.test` (prediction error measurements based on the testing data). Only available if train.prop is smaller than 1.

Arguments

x: a univariate time series or a `tslm` object.
trend.order: an integer specifying the polynomial order of the trend line estimation. If trend.order = 0, no trend component will be included in the model. Default is 1.
seasonal: logical. If TRUE, seasonal component will be included in the model. Default is FALSE.
period: a numerical value specifying the seasonal cycle length of the series. If omitted, frequency(x) will be used here. Only effective if seasonal = TRUE. Default is NA.
type: string characters specifying the series type. Available options are `additive` and `multiplicative`. If `type = multiplicative`, interaction terms between trend and seasonal components will be added to the model. Default is `additive`.
train.prop: a numerical value specifying the proportion of training data in the series. The value must be between 0 and 1. Default is 1.
digits: the number of significant digits to use when printing
...: other printing or summary parameters.
object: a tslm object for summary
anova: logical. If TRUE, an anova table with significance tests for trend, seasonality and their interaction will be included in the summary.
se: logical. If TRUE, standard error will be included in displaying the result. Default is TRUE.
signif.stars: logical. If TRUE, `significance stars` are printed for each coefficient

Author

Ka Yui Karl Wu

References

Hyndman, R. J., Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.
https://otexts.com/fpp3/

Examples

Run this code

tslm(airport$Travellers, trend.order = 2, 
     seasonal = TRUE, type = "multiplicative")

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