## ----cran_install, eval = FALSE-----------------------------------------------
# install.packages("forecastHybrid")

## ----github_install, eval = FALSE---------------------------------------------
# devtools::install_git("https://gitlab.com/dashaub/forecastHybrid", subdir = "pkg")

## ----load_library, message = FALSE--------------------------------------------
library(forecastHybrid) # nolint: undesirable_function_linter

## ----quickstart, cache = TRUE-------------------------------------------------
quickModel <- hybridModel(USAccDeaths)
forecast(quickModel)
plot(forecast(quickModel),
     main = "Forecast from auto.arima, ets, thetam, nnetar, stlm, and tbats model")

## ----basic_model, cache = TRUE------------------------------------------------

# Build a hybrid forecast on a simulated dataset using auto.arima, ets, and tbats models.
# Each model is given equal weight
set.seed(12345)
series <- ts(rnorm(18), f = 2)
hm1 <- hybridModel(y = series, models = "aet", weights = "equal")

## ----individualModels, cache = TRUE-------------------------------------------
# View the individual models
hm1$auto.arima

# See forecasts from the auto.arima model
plot(forecast(hm1$auto.arima))

## ----object_class, cache = TRUE-----------------------------------------------
class(hm1)
is.hybridModel(hm1)

## ----printSummary, cache = TRUE-----------------------------------------------
print(hm1)
summary(hm1)

## ----plots, cache = TRUE------------------------------------------------------
plot(quickModel, type = "fit")
plot(quickModel, type = "models")

## ----plots_ggplot, cache = TRUE-----------------------------------------------
plot(quickModel, type = "fit", ggplot = TRUE)
plot(quickModel, type = "models", ggplot = TRUE)

## ----errorMethod, cache = TRUE------------------------------------------------
hm2 <- hybridModel(series, weights = "insample.errors", errorMethod = "MASE", models = "aenst")
hm2

## ----viewWeights, cache = TRUE------------------------------------------------
hm2$weights
newWeights <- c(0.1, 0.2, 0.3, 0.1, 0.3)
names(newWeights) <- c("auto.arima", "ets", "nnetar", "stlm", "tbats")
hm2$weights <- newWeights
hm2
hm2$weights[1] <- 0.2
hm2$weights[2] <- 0.1
hm2

## ----generics, cache = TRUE---------------------------------------------------
# View the first 10 fitted values and residuals
head(fitted(hm1))
head(residuals(hm1))

## ----accuracy_ensemble, cache = TRUE------------------------------------------
accuracy(hm1)

## ----accuracy_individual, cache = TRUE----------------------------------------
accuracy(hm1, individual = TRUE)

## ----basic_forecast, cache = TRUE---------------------------------------------
hForecast <- forecast(hm1, h = 48)

## ----plot_forecast, cache = TRUE----------------------------------------------
plot(hForecast)

## ----advanced_fit, cache = TRUE-----------------------------------------------
hm3 <- hybridModel(y = series, models = "aefnst",
                   a.args = list(max.p = 12, max.q = 12, approximation = FALSE),
                   n.args = list(repeats = 50),
                   s.args = list(robust = TRUE),
                   t.args = list(use.arma.errors = FALSE))

## ----lambda, cache = TRUE-----------------------------------------------------
hm4 <- hybridModel(y = wineind, models = "ae", lambda = 0.15)
hm4$auto.arima$lambda
hm4$ets$lambda

## ----advancedLambda, cache = TRUE---------------------------------------------
hm5 <- hybridModel(y = USAccDeaths, models = "aens", lambda = 0.2,
                   a.args = list(lambda = 0.5),
                   n.args = list(lambda = 0.6))
hm5$auto.arima$lambda
hm5$ets$lambda
hm5$nnetar$lambda
hm5$stlm$lambda


## ----xreg, cache = TRUE-------------------------------------------------------
# Use the beaver1 dataset with the variable "activ" as a covariate and "temp" as the time series
# Divide this into a train and test set
trainSet <- beaver1[1:100, ]
testSet <- beaver1[101:110, ]
trainXreg <- matrix(trainSet$activ)
testXreg <- matrix(testSet$activ)

# Create the model
beaverhm <- hybridModel(ts(trainSet$temp, f = 6),
                        models = "aenst",
                        a.args = list(xreg = trainXreg),
                        n.args = list(xreg = trainXreg),
                        s.args = list(xreg = trainXreg, method = "arima"))
# Forecast future values
beaverfc <- forecast(beaverhm, xreg = testXreg, PI = FALSE)

# View the accuracy of the model
accuracy(beaverfc, testSet$temp)

## ----cvts_comparison, cache=TRUE----------------------------------------------
stlmMod <- cvts(woolyrnq, FUN = stlm, windowSize = 100, maxHorizon = 8)
naiveMod <- cvts(woolyrnq, FUN = naive, windowSize = 100, maxHorizon = 8)
accuracy(stlmMod)
accuracy(naiveMod)

## ----woolyrnq_plot------------------------------------------------------------
plot(woolyrnq)

## ----custom_cvts, cache=TRUE--------------------------------------------------
gmdhForecast <- function(x, h) {
  fc <- GMDH::fcast(x, f.number = h)
  # GMDH doesn't produce a ts object with correct attributes, so we build it
  endVal <- tsp(x)[2]
  freq <- frequency(x)
  # Set the correct start, end, and frequency for the ts forecast object
  tsProperties <- c(endVal + 1 / freq, endVal + h / freq, freq)
  tsp(fc$mean) <- tsProperties
  tsp(fc$upper) <- tsProperties
  tsp(fc$lower) <- tsProperties
  class(fc) <- "forecast"
  fc
}
series <- subset(woolyrnq, end = 12)
gmdhcv <- cvts(series, FCFUN = gmdhForecast, windowSize = 10, maxHorizon = 1)

## ----custom_naive_cvts, cache=TRUE--------------------------------------------
customMod <- function(x) {
  result <- list()
  result$series <- x
  result$last <- tail(x, n = 1)
  class(result) <- "customMod"
  result
}
forecast.customMod <- function(x, h = 12) {
  result <- list()
  result$model <- x
  result$mean <- rep(x$last, h)
  class(result) <- "forecast"
  result
}
series <- subset(AirPassengers, end = 94)
cvobj <- cvts(series, FUN = customMod, FCFUN = forecast.customMod)