hanaml.DoubleExponentialSmoothing {hana.ml.r}R Documentation

DoubleExponentialSmoothing

Description

hanaml.DoubleExponentialSmoothing is a R wrapper for PAL Double Exponential Smoothing algorithm.

Usage

hanaml.DoubleExponentialSmoothing (conn.context,
                                   data,
                                   key = NULL,
                                   endog = NULL,
                                   alpha = NULL,
                                   beta = NULL,
                                   forecast.num = NULL,
                                   phi = NULL,
                                   damped = NULL,
                                   accuracy.measure = NULL,
                                   ignore.zero = NULL,
                                   expost.flag = NULL,
                                   prediction.confidence.1 = NULL,
                                   prediction.confidence.2 = 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
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.

forecast.num

integer, optional
Number of values to be forecast.
Defaults to 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.

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.

Details

Double Exponential Smoothing model is suitable to model the time series with trend but without seasonality.
In the model there are two kinds of smoothed quantities: smoothed signal and smoothed trend.

Value

Return a list of two DataFrame:

Examples

## Not run: 
Input DataFrame data:
> data$Collect()
     ID RAWDATA
  1   1     143
  2   2     152
  3   3     161
  ......
  21 21     223
  22 22     242
  23 23     239
  24 24     266

 desm <- hanaml.DoubleExponentialSmoothing(conn.context = conn,
                                           data = data,
                                           alpha=0.501,
                                           beta=0.072,
                                           forecast.num=6,
                                           phi=NULL,
                                           damped=NULL,
                                           accuracy.measure='mse',
                                           ignore.zero=NULL,
                                           expost.flag=TRUE,
                                           prediction.confidence.1=0.8,
                                           prediction.confidence.2=0.95)
Output:
> desm[[2]]$Collect()
  STAT_NAME    STAT_VALUE
1       MSE      274.896

## End(Not run)
[Package hana.ml.r version 1.0.8 Index]