Package 'spooky'

spooky

Description

Automatic jack-knife of spectral analysis for time feature extrapolation

Usage

spooky(
  df,
  seq_len = NULL,
  lno = NULL,
  n_samp = 30,
  n_windows = 3,
  ci = 0.8,
  smoother = FALSE,
  dates = NULL,
  error_scale = "naive",
  error_benchmark = "naive",
  seed = 42
)

Arguments

df	A data frame with time features on columns
seq_len	Positive integer. Time-step number of the forecasting sequence. Default: NULL (automatic selection between 1 and the square root of full length).
lno	Positive integer. Number of data points to leave out for resampling (using jack-knife approach). Default: NULL (automatic selection between 1 and the square root of full length).
n_samp	Positive integer. Number of samples for random search. Default: 30.
n_windows	Positive integer. Number of validation windows to test prediction error. Default: 10.
ci	Confidence interval for prediction. Default: 0.8
smoother	Logical. Flag to TRUE for loess smoothing. Default: FALSE.
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 not-null 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 selected model according to the weighted average rank, including:

  • testing_errors: testing errors for each time feature for the best selected model (for continuous variables: me, mae, mse, rmsse, mpe, mape, rmae, rrmse, rame, mase, smse, sce, gmrae; for factor variables: czekanowski, tanimoto, cosine, hassebrook, jaccard, dice, canberra, gower, lorentzian, clark)

  • preds: for continuous variables, min, max, q25, q50, q75, quantiles at selected ci, mean, sd, mode, skewness, kurtosis, IQR to range, risk ratio, upside probability and divergence for each point fo predicted sequences; for factor variables, min, max, q25, q50, q75, quantiles at selected ci, proportions, difformity (deviation of proportions normalized over the maximum possible deviation), entropy, upgrade probability and divergence for each point fo predicted sequences

  • plots: standard plot with confidence interval for each time feature

• time_log

Author(s)

Giancarlo Vercellino [email protected]

See Also

Useful links:

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

Examples

spooky(time_features, seq_len = c(10, 30), lno = c(1, 30), n_samp = 1)

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time features example: IBM and Microsoft Close Prices

Description

A data frame with with daily with daily prices for IBM and Microsoft since March 2017.

Usage

time_features

Format

A data frame with 2 columns and 1324 rows.

Source

finance.yahoo.com

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