Package 'caretForecast'

Autoregressive forecasting using various Machine Learning models.

Description

Autoregressive forecasting using various Machine Learning models.

Usage

ARml(
  y,
  max_lag = 5,
  xreg = NULL,
  caret_method = "cubist",
  metric = "RMSE",
  pre_process = NULL,
  cv = TRUE,
  cv_horizon = 4,
  initial_window = NULL,
  fixed_window = FALSE,
  verbose = TRUE,
  seasonal = TRUE,
  K = frequency(y)/2,
  tune_grid = NULL,
  lambda = NULL,
  BoxCox_method = c("guerrero", "loglik"),
  BoxCox_lower = -1,
  BoxCox_upper = 2,
  BoxCox_biasadj = FALSE,
  BoxCox_fvar = NULL,
  allow_parallel = FALSE,
  calibrate = TRUE,
  calibration_horizon = NULL,
  n_cal_windows = NULL,
  ...
)

Arguments

y	A univariate time series object.
max_lag	Maximum value of lag.
xreg	Optional. A numerical vector or matrix of external regressors, which must have the same number of rows as y. (It should not be a data frame.).
caret_method	A string specifying which classification or regression model to use. Possible values are found using names(getModelInfo()). A list of functions can also be passed for a custom model function. See https://topepo.github.io/caret/ for details.
metric	A string that specifies what summary metric will be used to select the optimal model. See ?caret::train.
pre_process	A string vector that defines a pre-processing of the predictor data. Current possibilities are "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica" and "spatialSign". The default is no pre-processing. See preProcess and trainControl on the procedures and how to adjust them. Pre-processing code is only designed to work when x is a simple matrix or data frame.
cv	Logical, if cv = TRUE model selection will be done via cross-validation. If cv = FALSE user need to provide a specific model via tune_grid argument.
cv_horizon	The number of consecutive values in test set sample.
initial_window	The initial number of consecutive values in each training set sample.
fixed_window	Logical, if FALSE, all training samples start at 1.
verbose	A logical for printing a training log.
seasonal	Boolean. If seasonal = TRUE the fourier terms will be used for modeling seasonality.
K	Maximum order(s) of Fourier terms
tune_grid	A data frame with possible tuning values. The columns are named the same as the tuning parameters. Use getModelInfo to get a list of tuning parameters for each model or see https://topepo.github.io/caret/available-models.html. (NOTE: If given, this argument must be named.)
lambda	BoxCox transformation parameter. If lambda = NULL If lambda = "auto", then the transformation parameter lambda is chosen using BoxCox.lambda.
BoxCox_method	BoxCox.lambda argument. Choose method to be used in calculating lambda.
BoxCox_lower	BoxCox.lambda argument. Lower limit for possible lambda values.
BoxCox_upper	BoxCox.lambda argument. Upper limit for possible lambda values.
BoxCox_biasadj	InvBoxCox argument. Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.
BoxCox_fvar	InvBoxCox argument. Optional parameter required if biasadj=TRUE. Can either be the forecast variance, or a list containing the interval level, and the corresponding upper and lower intervals.
allow_parallel	If a parallel backend is loaded and available, should the function use it?
calibrate	Logical. If TRUE, performs rolling-origin calibration to compute horizon-specific conformal prediction intervals. This produces properly calibrated intervals that widen with forecast horizon (trumpet shape). Default is TRUE.
calibration_horizon	Maximum forecast horizon for calibration. If NULL (default), uses 2 * frequency(y) for seasonal data or 10 for non-seasonal data.
n_cal_windows	Number of rolling windows for calibration. If NULL (default), automatically determined based on data length (max 50).
...	Ignored.

Value

A list class of forecast containing the following elemets

• x : The input time series

• method : The name of the forecasting method as a character string

• mean : Point forecasts as a time series

• lower : Lower limits for prediction intervals

• upper : Upper limits for prediction intervals

• level : The confidence values associated with the prediction intervals

• model : A list containing information about the fitted model

• newx : A matrix containing regressors

• calibration : Horizon-specific conformal calibration scores (if calibrate=TRUE)

Author(s)

Resul Akay

Examples

library(caretForecast)

train_data <- window(AirPassengers, end = c(1959, 12))

test <- window(AirPassengers, start = c(1960, 1))

ARml(train_data, caret_method = "lm", max_lag = 12) -> fit

forecast(fit, h = length(test)) -> fc

autoplot(fc) + autolayer(test)

accuracy(fc, test)

---

Fit a conformal regressor.

Description

Fit a conformal regressor.

Usage

conformalRegressor(residuals, sigmas = NULL)

Arguments

residuals	Model residuals.
sigmas	A vector of difficulty estimates

Value

A conformalRegressor object

Author(s)

Resul Akay

References

Boström, H., 2022. crepes: a Python Package for Generating Conformal Regressors and Predictive Systems. In Conformal and Probabilistic Prediction and Applications. PMLR, 179.

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Fit a horizon-specific conformal regressor for time series forecasting.

Description

This function creates a conformal regressor that accounts for increasing uncertainty at longer forecast horizons. It uses separate nonconformity score distributions for each horizon h=1,2,3,..., resulting in prediction intervals that naturally widen as the forecast horizon increases (trumpet-shaped intervals).

Usage

conformalRegressorByHorizon(horizon_errors)

Arguments

horizon_errors

A named list where each element contains sorted absolute errors for that horizon. Names should be "h1", "h2", etc. This is typically produced by calibrate_horizon_scores().

Value

A conformalRegressorByHorizon object containing:

alphas_by_horizon	List of sorted nonconformity scores for each horizon
max_horizon	Maximum calibrated horizon
n_samples	Number of calibration samples per horizon

Author(s)

Resul Akay

References

Boström, H., 2022. crepes: a Python Package for Generating Conformal Regressors and Predictive Systems. In Conformal and Probabilistic Prediction and Applications. PMLR, 179.

Stankeviciute, K., Alaa, A. M., & van der Schaar, M., 2021. Conformal Time-series Forecasting. NeurIPS 2021.

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Forecasting using ARml model

Description

Forecasting using ARml model

Usage

## S3 method for class 'ARml'
forecast(object, h = frequency(object$y), xreg = NULL, level = c(80, 95), ...)

Arguments

object	An object of class "ARml", the result of a call to ARml.
h	forecast horizon
xreg	Optionally, a numerical vector or matrix of future external regressors
level	Confidence level for prediction intervals.
...	Ignored

Value

A list class of forecast containing the following elemets

• x : The input time series

• method : The name of the forecasting method as a character string

• mean : Point forecasts as a time series

• lower : Lower limits for prediction intervals

• upper : Upper limits for prediction intervals

• level : The confidence values associated with the prediction intervals

• model : A list containing information about the fitted model

• newxreg : A matrix containing regressors

Author(s)

Resul Akay

Examples

library(caretForecast)

train_data <- window(AirPassengers, end = c(1959, 12))

test <- window(AirPassengers, start = c(1960, 1))

ARml(train_data, caret_method = "lm", max_lag = 12) -> fit

forecast(fit, h = length(test), level = c(80,95)) -> fc

autoplot(fc)+ autolayer(test)

accuracy(fc, test)

---

Variable importance for forecasting model.

Description

Variable importance for forecasting model.

Usage

get_var_imp(object, plot = TRUE)

Arguments

object	A list class of ARml or forecast object derived from ARml
plot	Boolean, if TRUE, variable importance will be ploted.

Value

A list class of "varImp.train". See varImp or a "trellis" plot.

Author(s)

Resul Akay

Examples

train <- window(AirPassengers, end = c(1959, 12))

test <- window(AirPassengers, start = c(1960, 1))

ARml(train, caret_method = "lm", max_lag = 12,
 pre_process = "center") -> fit

forecast(fit, h = length(test), level = c(80,95)) -> fc

autoplot(fc)+ autolayer(test)

accuracy(fc, test)

get_var_imp(fc, plot = TRUE)

---

Predict a conformalRegressor

Description

Predict a conformalRegressor

Usage

## S3 method for class 'conformalRegressor'
predict(
  object,
  y_hat = NULL,
  sigmas = NULL,
  confidence = 0.95,
  y_min = -Inf,
  y_max = Inf,
  ...
)

Arguments

object	A conformalRegressor object
y_hat	Predicted values
sigmas	Difficulty estimates
confidence	Confidence level
y_min	The minimum value to include in prediction intervals
y_max	The maximum value to include in prediction intervals
...	Ignored

Value

Prediction intervals

Author(s)

Resul Akay

---

Predict intervals from a horizon-specific conformal regressor

Description

This function generates prediction intervals that account for increasing uncertainty at longer forecast horizons. Each horizon h uses its own calibrated nonconformity score distribution, resulting in trumpet-shaped prediction intervals.

Usage

## S3 method for class 'conformalRegressorByHorizon'
predict(
  object,
  y_hat = NULL,
  confidence = 0.95,
  y_min = -Inf,
  y_max = Inf,
  ...
)

Arguments

object	A conformalRegressorByHorizon object
y_hat	Predicted values (one per horizon)
confidence	Confidence level(s) between 0 and 1 (e.g., 0.95 for 95 percent)
y_min	The minimum value to include in prediction intervals
y_max	The maximum value to include in prediction intervals
...	Ignored

Value

A data frame with lower and upper bounds for each confidence level

Author(s)

Resul Akay

---

Grouped sales data from an Australian Retailer

Description

A dataset containing 42 products' sales

Usage

retail

Format

A data class of "tbl_df", "tbl", "data.frame" with 13986 rows and 3 columns:

date

date

item

products

value

sales

Source

https://robjhyndman.com/data/ausretail.csv

---

Sales data from an Australian Retailer in time series format

Description

A dataset containing 42 products' sales

Usage

retail_wide

Format

An object of class mts (inherits from ts, matrix) with 333 rows and 43 columns.

This data set is the wide format of retail data.

Source

https://robjhyndman.com/data/ausretail.csv

---

Split a time series into training and testing sets

Description

Split a time series into training and testing sets

Usage

split_ts(y, test_size = 10)

Arguments

y	A univariate time series
test_size	The number of observations to keep in the test set

Value

A list with train and test elements

Author(s)

Resul Akay

Examples

dlist <- split_ts(retail_wide[,1], test_size = 12)

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Suggested methods for ARml

Description

Suggested methods for ARml

Usage

suggested_methods()

Value

A character vector of Suggested methods

Author(s)

Resul Akay

Examples

suggested_methods()

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