Description Usage Arguments Value See Also Examples
Train multiple time series datasets using multiple algorithms
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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> |
Object of class "karma.fry"
1 | kfried = karma.fry(list(JohnsonJohnson, mdeaths, ldeaths))
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