karma.fry: Train multiple time series datasets using multiple algorithms

In snarf-snarf/karma: Complementary tools for automated time-series machine learning.

Description

Train multiple time series datasets using multiple algorithms

Usage

karma.fry(yt_list, test_pct = "auto", test_type = "auto", xreg = NULL,
  stdout = F)

Arguments

yt_list	A list of univariate time-series vectors; type <numeric> or <ts>
test_pct	Percentage of train-test split in cross-validation (e.g. 70-30), positive integer for "window" or "percentage" test_type; "auto" to read from karma.fit object or generate; negative integer value to set window size to a multiple of the series' frequency.
test_type	Train-test split type, i.e. percentage or fixed window; "auto": will try to read from karma.fit object or generate; "percentage": test_pct = 12 will be read as the 12 percent of the length of the series; "window": test_pct = 12 will be read as the 12 last time points (e.g. months) of the series; "auto" if input series is a ts() object, test_type is set to "window" and test_pct is set to twice the frequency of the series - if test_pct is given a negative factor, then test_pct (window size) will be set to the frequency of the series times the absolute value of that negative number.
xreg	Optional vector or matrix of exogenous regressors; see documentation for Arima(), package 'forecast'.
stdout	Option to report training status in the standard output; <logical>

Value

Object of class "karma.fry"

See Also

tseries, forecast

Examples

kfried = karma.fry(list(JohnsonJohnson, mdeaths, ldeaths))

snarf-snarf/karma documentation built on May 24, 2019, 7:19 a.m.