hanaml.BrownExponentialSmoothing.Rd
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
)
DataFrame
DataFrame containting the data.
character, optional
Name of the ID column.
Defaults to the first column if not provided.
character, optional
The endogenous variable, i.e. time series.
Defaults to the first non-ID column.
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
double, optional
Value of weighted for At and Mt.
Only valid when 'adaptive_method' is TRUE.
Defaults to 0.2
integer, optional
Number of values to be forecast.
Defaults to 0.
logical, optional
FALSE: Brown exponential smoothing.
TRUE: Adaptive brown exponential smoothing.
Defaults to FALSE.
character or list of characters, optional
Specifies the method of accuracy evaluation.
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.
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.
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.
Return a list of two DataFrames:
DataFrame 1
Forecast values.
DataFrame 2
Statistics analysis content.
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.
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