Package 'jenga'

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

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jenga: automatic projections of time features using modern KNN

Description

Automatic projections of time features using approximate nearest neighbors, optional GPU acceleration, probabilistic forecasts, conformal intervals, and multiscale sequence representations.

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,
  ann = "auto",
  gpu = "auto",
  probabilistic = TRUE,
  conformal = TRUE,
  multiscale = c(1, 2, 4),
  n_draws = 1000
)

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	String. Distance method for calculating neighbors. Possible options are: "euclidean", "manhattan", "minkowski", "chebyshev", "canberra", "clark", "sorensen", "lorentzian", "cosine", "correlation", and "hamming". Default: NULL (automatic selection).
kernel	String. Kernel used to calculate neighbor weights. Possible options are: "norm", "cauchy", "logis", "unif", "t", "laplace", "epanechnikov", "triangular", "biweight", "triweight", and "cosine". 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.
ann	String. Approximate nearest-neighbor backend. Options are: "auto", "exact", and "projection". Default: "auto".
gpu	String. Acceleration policy for distance calculations. Options are: "auto", "off", and "torch". Default: "auto".
probabilistic	Logical. Return simulated forecast distributions and probabilistic summaries. Default: TRUE.
conformal	Logical. Add split-conformal forecast intervals calibrated on validation windows. Default: TRUE.
multiscale	Positive integer vector. Sequence aggregation scales used in the neighbor representation. Default: c(1, 2, 4).
n_draws	Positive integer. Number of simulated draws for probabilistic forecasts. Default: 1000.

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)

• distribution: simulated forecast draws for each feature when probabilistic = TRUE

• settings: v2 execution settings for ANN, GPU, conformal, probabilistic and multiscale options

• time_log

Author(s)

Giancarlo Vercellino [email protected]

See Also

Useful links:

• https://rpubs.com/giancarlo_vercellino/jenga

Examples

jenga(covid_in_europe[, c(2, 3)], n_sample = 1)
jenga(covid_in_europe[, c(4, 5)], n_sample = 1)

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Plot jenga forecast objects

Description

Plot jenga forecast objects

Usage

## S3 method for class 'jenga_plot'
plot(x, ...)

Arguments

x	A jenga forecast plot object.
...	Additional arguments passed to the base plotting device.

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Print jenga forecast plot objects

Description

Print jenga forecast plot objects

Usage

## S3 method for class 'jenga_plot'
print(x, ...)

Arguments

x	A jenga forecast plot object.
...	Additional arguments passed to the base plotting device.

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