| Type: | Package |
| Title: | Fast Extrapolation of Time Features using K-Nearest Neighbors |
| Version: | 1.3.0 |
| Author: | Giancarlo Vercellino |
| Maintainer: | Giancarlo Vercellino <giancarlo.vercellino@gmail.com> |
| Description: | Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 4.1) |
| Imports: | purrr (≥ 0.3.4), abind (≥ 1.4-5), ggplot2 (≥ 3.3.5), readr (≥ 2.1.2), lubridate (≥ 1.4.0), narray (≥ 0.4.1.1), imputeTS (≥ 3.2), scales (≥ 1.1.1), tictoc (≥ 1.0.1), modeest (≥ 2.4.0), moments (≥ 0.14), philentropy (≥ 0.5.0), greybox (≥ 1.0.1), Rfast (≥ 2.0.6), dplyr(≥ 1.0.7), fastDummies (≥ 1.6.3), fANCOVA (≥ 0.6-1), entropy (≥ 1.3.1) |
| URL: | https://rpubs.com/giancarlo_vercellino/jenga |
| NeedsCompilation: | no |
| Packaged: | 2022-08-18 07:55:55 UTC; gvercellino |
| Repository: | CRAN |
| Date/Publication: | 2022-08-18 08:10:02 UTC |
jenga: automatic projections of time features using KNN
Description
Automatic projections of time features using KNN
Usage
jenga(
df,
seq_len = NULL,
smoother = FALSE,
k = NULL,
method = NULL,
kernel = NULL,
ci = 0.8,
n_windows = 10,
mode = NULL,
n_sample = 30,
search = "random",
dates = NULL,
error_scale = "naive",
error_benchmark = "naive",
seed = 42
)
Arguments
df |
A data frame with time features on columns (numerical or categorical features, but not both). |
seq_len |
Positive integer. Time-step number of the projected sequence |
smoother |
Logical. Perform optimal smoothing using standard loess (only for numerical features). Default: FALSE |
k |
Positive integer. Number of neighbors to consider when applying kernel average. Min number is 3. Default: NULL (automatic selection). |
method |
Positive integer. Distance method for calculating neighbors. Possibile options are: "euclidean", "manhattan", "minkowski". Default: NULL (automatic selection). |
kernel |
String. Distribution used to calculate kernel densities. Possible options are: "norm", "cauchy", "unif", "t". Default: NULL (automatic selection). |
ci |
Confidence interval. Default: 0.8 |
n_windows |
Positive integer. Number of validation tests to measure/sample error. Default: 10. |
mode |
String. Sequencing method: deterministic ("segmented"), or non-deterministic ("sampled"). Default: NULL (automatic selection). |
n_sample |
Positive integer. Number of samples for grid or random search. Default: 30. |
search |
String. Two option available: "grid", "random". Default: "random". |
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: list of all models, complete with predictions, test metrics, prediction stats and plot
history: a table with the sampled models, hyper-parameters, validation errors
best_model: results for the best model, including:
predictions: min, max, q25, q50, q75, quantiles at selected ci, and different statics for numerical and categorical variables
testing_errors: training and testing errors for one-step and sequence for each ts feature (different measures for numerical and categorical variables)
time_log
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
See Also
Useful links:
Examples
jenga(covid_in_europe[, c(2, 3)], n_sample = 1)
jenga(covid_in_europe[, c(4, 5)], n_sample = 1)
covid_in_europe data set
Description
A data frame with with daily and cumulative cases of Covid infections and deaths in Europe since March 2021.
Usage
covid_in_europe
Format
A data frame with 5 columns and 163 rows.
Source
www.ecdc.europa.eu