Triple Exponential Smoothing

hanaml.TripleExponentialSmoothing is a R wrapper for SAP HANA Triple Exponential Smoothing algorithm.

hanaml.TripleExponentialSmoothing(
  data,
  key = NULL,
  endog = NULL,
  alpha = NULL,
  beta = NULL,
  gamma = NULL,
  seasonal.period = NULL,
  forecast.num = NULL,
  seasonal = NULL,
  initial.method = NULL,
  phi = NULL,
  damped = NULL,
  accuracy.measure = NULL,
  ignore.zero = NULL,
  expost.flag = NULL,
  level.start = NULL,
  trend.start = NULL,
  season.start = 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.
beta	double, optional Weight for the trend component. Value range: 0 <= beta < 1. Defaults to 0.1.
gamma	double, optional Weight for the seasonal component. Value range: 0 < gamma < 1. Defaults to 0.1.
seasonal.period	integer, optional Length of a seasonal.period (L > 1). For example, the seasonal.period of quarterly data is 4, and the seasonal.period of monthly data is 12. Defaults to 2.
forecast.num	integer, optional Number of values to be forecast. Defaults to 0.
seasonal	integer, optional 0: Multiplicative triple exponential smoothing. 1: Additive triple exponential smoothing. When seasonal is set to 1, the default value of initial.method is 1; When seasonal is set to 0, the default value of initial.method is 0. Defaults to 0.
initial.method	integer, optional Initialization method for the trend and seasonal components. When seasonal is set to 1, the default value of initial.method is 1; When seasonal is set to 0, the default value of initial.method is 0.
phi	double, optional Value of the damped smoothing constant phi (0 < phi < 1). Defaults to 0.1.
damped	logical, optional FALSE: Uses the Holt Winter method. TRUE: Uses the additive damped seasonal Holt Winter method. 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.
level.start	double, optional The initial value for level component S. If this value is not provided, it will be calculated in the way as described in Triple Exponential Smoothing. level.start cannot be zero. If it is set to zero, 0.0000000001 will be used instead.
trend.start	double, optional The initial value for trend component B. No default value.
season.start	list of tuples, optional A list of initial values for seasonal component C. Two values must be provided for each cycle: Cycle ID: An int which represents which cycle the initial value is used for. Initial value: A double precision number which represents the initial value for the corresponding cycle. For example: To give the initial value 0.5 to the 3rd cycle, insert list(tuple(3, 0.5)) into the parameter table. No default value.
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

Returns a list of two DataFrame:

• DataFrame 1
  Forecast values.

• DataFrame 2
  Statistics analysis content.

Details

Triple Exponential smoothing is used to handle the time series data containing a seasonal component.

Examples

Input DataFrame data:

> data$Collect()
     ID RAWDATA
1   1     362
2   2     385
3   3     432
......
22 22     725
23 23     854
24 24     661

Call the function:

> tesm <- hanaml.TripleExponentialSmoothing(data = data,
                                            alpha=0.822,
                                            beta=0.055,
                                            gamma=0.055,
                                            seasonal.period=4,
                                            forecast.num=6,
                                            seasonal=0,
                                            initial.method=0,
                                            phi=NULL,
                                            damped=NULL,
                                            accuracy.measure="mse",
                                            ignore.zero=NULL,
                                            expost.flag=TRUE,
                                            level.start=NULL,
                                            trend.start=NULL,
                                            season.start=NULL,
                                            prediction.confidence.1=0.8,
                                            prediction.confidence.2=0.95)

Output:

> tesm[[2]]$Collect()
  STAT_NAME    STAT_VALUE
1       MSE      616.5415