dynrmf: Dynamic regression model

In Techtonique/ahead: Time Series Forecasting with uncertainty quantification

Dynamic regression model

Description

Adapted from forecast::nnetar, with alternative fitting functions (see examples)

Usage

dynrmf(
  y,
  h = 5,
  level = 95,
  fit_func = ahead::ridge,
  predict_func = predict,
  fit_params = NULL,
  type_pi = c("gaussian", "E", "A", "T"),
  xreg_fit = NULL,
  xreg_predict = NULL,
  ...
)

Arguments

y	A numeric vector or time series of class ts
h	Forecasting horizon
level	Confidence level for prediction intervals
fit_func	Fitting function (Statistical/ML model). Default is Ridge regression.
predict_func	Prediction function (Statistical/ML model)
fit_params	a list of additional parameters for the fitting function fit_func (see examples)
type_pi	Type of prediction interval (currently "gaussian", ETS: "E", Arima: "A" or Theta: "T")
xreg_fit	Optionally, a vector or matrix of external regressors, which must have the same number of rows as y. Must be numeric.
xreg_predict	Future values of external regressor variables.
...	additional parameters

Value

a list; an object of class forecast.

The function summary is used to obtain and print a summary of the results.

The generic accessor functions fitted.values and residuals extract useful features.

Author(s)

T. Moudiki

References

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.

Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2021). forecast: Forecasting functions for time series and linear models. R package version 8.14, <URL: https://pkg.robjhyndman.com/forecast/>.

Examples

# Example 0: with Ridge regression

par(mfrow=c(3, 2))
plot(dynrmf(USAccDeaths, h=20, level=95))
plot(dynrmf(AirPassengers, h=20, level=95))
plot(dynrmf(lynx, h=20, level=95))
plot(dynrmf(WWWusage, h=20, level=95))
plot(dynrmf(Nile, h=20, level=95))
plot(dynrmf(fdeaths, h=20, level=95))


# Example 1: with Random Forest

## Not run: 

require(randomForest)

par(mfrow=c(3, 2))
plot(dynrmf(USAccDeaths, h=20, level=95, fit_func = randomForest::randomForest,
     fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(AirPassengers, h=20, level=95, fit_func = randomForest::randomForest,
     fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(lynx, h=20, level=95, fit_func = randomForest::randomForest,
     fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(WWWusage, h=20, level=95, fit_func = randomForest::randomForest,
     fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(Nile, h=20, level=95, fit_func = randomForest::randomForest,
     fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = randomForest::randomForest,
     fit_params = list(ntree = 50), predict_func = predict))

## End(Not run)

# Example 2: with SVM

## Not run: 

require(e1071)

par(mfrow=c(2, 2))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "linear"), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "polynomial"), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "radial"), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "sigmoid"), predict_func = predict))


## End(Not run)

Techtonique/ahead documentation built on Nov. 24, 2024, 10:33 a.m.