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:

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