hanaml.Croston.Rd
hanaml.Croston is a R wrapper for SAP HANA PAL Croston algorithm.
hanaml.Croston(
data,
key = NULL,
endog = NULL,
alpha = NULL,
forecast.num = NULL,
method = NULL,
accuracy.measure = NULL,
ignore.zero = NULL,
expost.flag = NULL
)
DataFrame
DataFrame containting the data.
character, optional
Name of the ID column.
Defaults to the first column if not provided.
character, optional
The endogenous variable, i.e. time series.
Defaults to the first non-ID column.
double, optional
Value of the smoothing constant alpha (0 < alpha < 1).
Defaults to 0.1.
integer, optional
Number of values to be forecast.
Defaults to 0.
character, optional
'sporadic': Use the sporadic method.
'constant': Use the constant method.
Defaults to "sporadic".
character or list of characters, optional
Specifies the method of accuracy evaluation.
mpe
: Mean percentage error.
mse
: Mean squared error.
rmse
: Root mean squared error.
et
: Error total.
mad
: Mean absolute deviation.
mase
: Out-of-sample mean absolute scaled error.
wmape
: Weighted mean absolute percentage error.
smape
: Symmetric mean absolute percentage error.
mape
: Mean absolute percentage error.
No default value.
logical, optional
FALSE: Uses zero values in the input dataset when calculating "mpe" or "mape".
TRUE: Ignores zero values in the input dataset when calculating "mpe" or "mape".
Only valid when accuracy.measure
is "mpe" or "mape".
Defaults to FALSE.
logical, optional
FALSE: Does not output the expost forecast, and just outputs the forecast values.
TRUE: Outputs the expost forecast and the forecast values.
Defaults to TRUE.
Return a list of two DataFrames:
DataFrame 1
Forecast values.
DataFrame 2
Statistics analysis content.
The Croston method is a forecast strategy for products with intermittent demand.
The Croston method consists of two steps. First, separate
exponential smoothing estimates are made of the average size of a demand.
Second, the average interval between demands is calculated.
This is then used in a form of the constant model to predict the future demand.
Input DataFrame data:
> data$Collect()
ID RAWDATA
1 0 0
2 1 1
3 2 4
4 3 0
5 4 0
6 5 0
7 6 5
8 7 3
9 8 0
10 9 0
11 10 0
Call the function:
> cesm <- hanaml.Croston(data = data,
alpha=0.1,
forecast.num=1,
method="sporadic",
accuracy.measure="mape")
Output:
> cesm[[2]]$Collect()
STAT_NAME STAT_VALUE
1 MAPE 0.2432182