Brown Exponential Smoothing

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

hanaml.BrownExponentialSmoothing(
  data,
  key = NULL,
  endog = NULL,
  alpha = NULL,
  delta = NULL,
  forecast.num = NULL,
  adaptive.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 The smoothing constant alpha for brown exponential smoothing or the initialization value for adaptive brown exponential smoothing (0 < alpha < 1). Defaults to 0.1 when Brown exponential smoothing Defaults to 0.2 when Adaptive brown exponential smoothing
delta	double, optional Value of weighted for At and Mt. Only valid when 'adaptive_method' is TRUE. Defaults to 0.2
forecast.num	integer, optional Number of values to be forecast. Defaults to 0.
adaptive.method	logical, optional FALSE: Brown exponential smoothing. TRUE: Adaptive brown exponential smoothing. Defaults to FALSE.
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 brown exponential smoothing model is suitable to model the time series with trend but without seasonality. In SAP HANA PAL, both non-adaptive and adaptive brown linear exponential smoothing are provided.

Examples

Input DataFrame data:

> data$Collect()
   ID RAWDATA
1   1     143
2   2     152
3   3     161
4   4     139
......
20 20     227
21 21     223
22 22     242
23 23     239
24 24     266

Call the function

> besm <- hanaml.BrownExponentialSmoothing(data = data,
                                           alpha=0.1,
                                           forecast.num=6,
                                           adaptive.method=FALSE,
                                           accuracy.measure="mse",
                                           expost.flag=TRUE)

Output:

> besm[[2]]$Collect()
  STAT_NAME    STAT_VALUE
1       MSE       474.142