Croston

hanaml.Croston is a R wrapper for SAP HANA PAL Croston Exponential Smoothing algorithm.

hanaml.Croston(
  data,
  key = NULL,
  endog = NULL,
  alpha = NULL,
  forecast.num = NULL,
  method = NULL,
  accuracy.measure = NULL,
  ignore.zero = NULL,
  expost.flag = NULL
)

Arguments

data	DataFrame DataFrame containting the data.
key	character, optional Name of the ID column. Defaults to the first column if not provided.
endog	character, optional The endogenous variable, i.e. time series. Defaults to the first non-ID column.
alpha	double, optional Value of the smoothing constant alpha (0 < alpha < 1). Defaults to 0.1.
forecast.num	integer, optional Number of values to be forecast. Defaults to 0.
method	character, optional 'sporadic': Use the sporadic method. 'constant': Use the constant method. Defaults to "sporadic".
accuracy.measure	character or list of characters, optional Specifies measure name. 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.
ignore.zero	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.
expost.flag	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.

Value

Return a list of two DataFrame:

• DataFrame 1
  Forecast values.

• DataFrame 2
  Statistics analysis content.

Details

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.

Examples

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