## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 4)

## -----------------------------------------------------------------------------
library(foresightr)
y <- AirPassengers

## -----------------------------------------------------------------------------
model_log(model_airline())

## -----------------------------------------------------------------------------
forecast_model(model_seasonal_naive(growth = TRUE), y, 6)

## -----------------------------------------------------------------------------
fit_model(model_holt_winters(), y)$params

## -----------------------------------------------------------------------------
fit <- fit_model(model_auto_ets(), y)
fit$details$code
fit$details[c("alpha", "beta", "gamma")]

## -----------------------------------------------------------------------------
fit <- fit_model(model_auto_arima(), log(y))
c(fit$details$order, fit$details$seasonal_order)
fit$aicc
exp(predict(fit, 6))

## -----------------------------------------------------------------------------
fit <- fit_model(model_prophet(), log(y))
fit$details$changepoints

## -----------------------------------------------------------------------------
fit <- fit_model(model_tbats(), y)
fit$details[c("harmonics", "lambda", "trend", "arma")]

## -----------------------------------------------------------------------------
demand <- c(0, 0, 3, 0, 0, 0, 2, 0, 0, 4, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0)
forecast_model(model_croston("sba"), demand, 3)

## -----------------------------------------------------------------------------
model_name(model_log(model_decomposed(model_auto_ets())))
forecast_model(model_box_cox(model_theta(), "guerrero"), y, 3)

## -----------------------------------------------------------------------------
fit <- fit_model(model_ensemble(candidates_default(), weighting = "stacked"), y)
round(fit$params[fit$params > 0], 3)

## -----------------------------------------------------------------------------
vapply(candidates_default(), model_name, "")
length(candidates_thorough())