R: Croston

hanaml.Croston {hana.ml.r}

R Documentation

Croston

Description

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

Usage

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

Arguments

`conn.context`	`ConnectionContext` The connection to the SAP HANA system.
`data`	`DataFrame` DataFrame containing the data.
`key`	`character, optional` Name of the ID column. Defaults to the first column.
`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.

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.

Value

Return a list of two DataFrame:

DataFrame 1
Forecast values.
DataFrame 2
Statistics analysis content.

Examples

## Not run: 
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

 cesm <- hanaml.Croston(conn.context = conn,
                         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

## End(Not run)

[Package hana.ml.r version 1.0.8 Index]