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

TripleExponentialSmoothing

Description

hanaml.TripleExponentialSmoothing is a R wrapper for PAL Triple Exp Smoothing algorithm.

Usage

hanaml.TripleExponentialSmoothing (conn.context,
                                   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

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.

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 of tuple [(3,5)] 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.

Format

R6Class object.

Details

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

Value

Return a list of two DataFrame:

Examples

## Not run: 
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

tesm <- hanaml.TripleExponentialSmoothing(conn.context = conn,
                                          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

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