Description Usage Arguments Details Value Examples
Applies four modelling strategies (ARIMA, ETS, Theta and naive or seasonally naive) to a dataset
with class Mdata
, returns accuracy statistics and (optionally) a summary graphic
1 | forecast_comp(the_series, tests = list(the_series$h), plot = FALSE, ...)
|
the_series |
a list of class |
tests |
a list of the forecast horizons over which to return the MAPE and MASE, passed to |
plot |
whether or not to draw basic plot of the four forecast model |
... |
other parameters to pass to |
This is just a convenience function for fitting four different standard time series forecasts to an object from an M competition or the tourism competition. Mainly of interest to analysis wishing to re-produce published results or create a benchmark against which other methods can be compared.
A data frame of eight rows and length(tests) + 1
columns with first column as method and each other column containing the
Mean Absolute Percentage Error (MAPE, first four rows) and Mean Absolute Scaled Error (MASE, final four rows) at the horizon
indicated by the column name
1 2 | forecast_comp(tourism$Y18, test = list(1, 2, 3, 4, 1:2, 1:4), plot = TRUE)
forecast_comp(tourism$Q4, test = list(1, 2, 3, 4, 5, 6, 7, 8, 1:4, 1:8), plot = FALSE)
|
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