naive {naive} | R Documentation |
naive
Description
Empirical Extrapolation of Time Feature Pattern
Usage
naive(
df,
seq_len = NULL,
ci = 0.8,
smoother = FALSE,
cover = NULL,
stride = NULL,
method = NULL,
location = NULL,
n_windows = 10,
n_samp = 30,
dates = NULL,
error_scale = "naive",
error_benchmark = "naive",
seed = 42
)
Arguments
df |
A data frame with time features on columns (all numerics or all categories, but not both). In case of missing values, automatic missing imputation through kalman filter will be performed. |
seq_len |
Positive integer. Time-step number of the forecasting sequence. Default: NULL (random selection within boundaries). |
ci |
Confidence interval for prediction. Default: 0.8 |
smoother |
Logical. Flag to TRUE for loess smoothing (only for numeric series). Default: FALSE. |
cover |
Positive numeric. The quantile cover around the location parameter (between 0 and 1). Default: NULL (random selection within boundaries). |
stride |
Positive integer. Shift between subsequent sequences. Default: NULL (random selection within boundaries). |
method |
String. Distance method using during the comparison of time sequences. Possible options are: "euclidean", "manhattan", "minkowski". Default: NULL (random selection). |
location |
String. Statistic used to center the cover parameter. Possible options are: "mean", "mode" (parzen method), "median". Default: NULL (random selection). |
n_windows |
Positive integer. Number of validation windows to test prediction error. Default: 10. |
n_samp |
Positive integer. Number of sample selected during random search. Default: 30. |
dates |
Date. Vector with dates for time features. |
error_scale |
String. Scale for the scaled error metrics. Two options: "naive" (average of naive one-step absolute error for the historical series) or "deviation" (standard error of the historical series). Default: "naive". |
error_benchmark |
String. Benchmark for the relative error metrics. Two options: "naive" (sequential extension of last value) or "average" (mean value of true sequence). Default: "naive". |
seed |
Positive integer. Random seed. Default: 42. |
Value
This function returns a list including:
exploration: collection of all the models explored with random search
history: a table with the explored models' hyper-parameters and validation errors
best_model: best combination resulting from the average prediction score across different ranks and features, including:
quant_preds: min, max, q25, q50, q75, quantiles at selected ci, mean, sd, mode, skewness, kurtosis, IQR to range, above to below median range, upside probability and divergence for each point fo predicted sequences
errors: testing errors for each time feature averaged across validation windows
plots: standard plot with confidence interval for each time feature
time_log
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
Maintainer: Giancarlo Vercellino giancarlo.vercellino@gmail.com [copyright holder]
See Also
Useful links:
Examples
{
naive(time_features[, 2:3, drop = FALSE], seq_len = 30, n_samp = 1, n_windows = 5)
}