Single Exponential Smoothing

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

hanaml.SingleExponentialSmoothing(
  data,
  key = NULL,
  endog = NULL,
  alpha = NULL,
  delta = NULL,
  forecast.num = NULL,
  adaptive.method = NULL,
  accuracy.measure = NULL,
  ignore.zero = NULL,
  expost.flag = NULL,
  prediction.confidence.1 = NULL,
  prediction.confidence.2 = 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 Weight for smoothing. Value range: 0 < alpha < 1. Defaults to 0.1 when adaptive.method is not TRUE, 0.2 otherwise.
delta	double, optional Value of weight 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: Single exponential smoothing TRUE: Adaptive-response-rate single 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.
prediction.confidence.1	double, optional Prediction confidence for interval 1. Only valid when the upper and lower columns are provided in the result table. Defaults to 0.8.
prediction.confidence.2	double, optional Prediction confidence for interval 2. Only valid when the upper and lower columns are provided in the result table. Defaults to 0.95.

Value

Return a list of two DataFrame:

• DataFrame 1
  Forecast values.

• DataFrame 2
  Statistics analysis content.

Details

Single Exponential Smoothing model is suitable to model the time series without trend and seasonality. In the model, the smoothed value is the weighted sum of previous smoothed value and previous observed value.
PAL provides two simple exponential smoothing algorithms: single exponential smoothing and adaptive-response-rate simple exponential smoothing. The adaptive-response-rate single exponential smoothing algorithm may have an advantage over single exponential smoothing in that it allows the value of alpha to be modified.

Examples

Input DataFrame data:

> data$Collect()
     ID RAWDATA
 1   1   200.0
 2   2   135.0
 3   3   195.0
 4   4   197.5
 5   5   310.0
 6   6   175.0
 7   7   155.0
 8   8   130.0
 9   9   220.0
 10 10   277.5
 11 11   235.0

Call the function:

> sesm <- hanaml.SingleExponentialSmoothing(alpha = 0.1,
                                            delta = 0.2,
                                            forecast.num = 12,
                                            adaptive.method = FALSE,
                                            accuracy.measure = list("MPE", "MSE"),
                                            ignore.zero = TRUE,
                                            expost.flag = TRUE,
                                            prediction.confidence.1 = 0.8,
                                            prediction.confidence.2 = 0.95)

Output:

> sesm[[2]]$Collect()
  STAT_NAME    STAT_VALUE
1       MPE   -0.05117142
2       MSE 3438.33212531