Fit a model and forecast

fit_model() estimates a model on a series and keeps what was estimated; predict() forecasts from it. forecast_model() does both in one go.

Usage

fit_model(model, y, period = NULL)

# S3 method for class 'foresight_fit'
predict(object, h = 12, ...)

forecast_model(model, y, h = 12, period = NULL)

Arguments

model

A model, e.g. model_theta().

y

A ts or a numeric vector.

period

The seasonal period when y is a plain vector (1 for none); a ts brings its own.

object

A fit from fit_model().

h

Periods to forecast.

...

Not used.

Value

fit_model(): an object of class foresight_fit, a list with the model name and description, the estimated params (named numeric), log_likelihood, aic, aicc, bic and residuals when the model has them (NA otherwise; the residuals of ARIMA are those of the differenced series, so there are fewer of them than observations), and details: orders and coefficients for ARIMA, the code and smoothing for ETS, changepoints (positions from 1) and event effects for Prophet, the structure for TBATS and its minus_two_log_likelihood (up to a constant; the lower the better, as its AIC). forecast_model(): the forecasts, a ts when y is one. Both fail when a forecast is not finite, as when regressors do not reach the horizon.

Details

The estimate lives in memory: a fit restored with readRDS() cannot forecast and has to be fitted again.

Examples

fit <- fit_model(model_log(model_airline()), AirPassengers)
predict(fit, h = 12)
#>           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
#> 1961 450.4223 425.7170 479.0062 492.4044 509.0549 583.3447 670.0108 667.0775
#>           Sep      Oct      Nov      Dec
#> 1961 558.1891 497.2077 429.8718 477.2423
forecast_model(model_theta(), AirPassengers, h = 3)
#>           Jan      Feb      Mar
#> 1961 440.0767 428.3829 489.7055