Linear Regression with Damped Trend and Seasonal Adjust

hanaml.LRSeasonalAdjust is a R wrapper for SAP HANA PAL Linear regression with damped trend and seasonal adjust.

hanaml.LRSeasonalAdjust(
  data,
  key,
  endog = NULL,
  forecast.length = NULL,
  trend = NULL,
  affect.future.only = NULL,
  seasonality = NULL,
  seasonal.period = NULL,
  accuracy.measure = NULL,
  seasonal.handle.method = NULL,
  expost.flag = NULL,
  ignore.zero = NULL
)

Arguments

data	DataFrame DataFrame containting the data.
key	character Name of the ID column.
endog	character, optional The endogenous variable, i.e. time series. Defaults to the first non-ID column.
forecast.length	integer, optional Length of the final forecast results. Defaults to 1.
trend	double, optional Damped trend factor. Value range is (0,1]. Defaults to 1.
affect.future.only	logical, optional Specifies whether the damped trend affects the history. FALSE: Affects all. TRUE: Affects the future only. Defaults to TRUE.
seasonality	integer, optional Specifies whether the data represents seasonality. 0: Non-seasonality. 1: Seasonality exists and user inputs the value of periods. 2: Automatically detects seasonality. Defaults to 0.
seasonal.period	integer, optional Length of seasonal period. seasonal.period is only valid when seasonality is 1. If this parameter is not specified, the seasonality value will be changed from 1 to 2, that is, from user-defined to automatically-detected. No default value.
accuracy.measure	character or list of character, optional Specifies accuracy measure.The criterion used for the optimization. "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.
seasonal.handle.method	character, optional Method used for calculating the index value in the seasonal_period. "average": Average method. "lr": Fitting linear regression. Defaults to "average".
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.
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". Defaults to FALSE.

Value

Returns a list of two DataFrames:

• DataFrame 1
  Forecast values.

• DataFrame 2
  Statistics analysis content.

Examples

Input DataFrame data:

> data$Collect()
  TIMESTAMP         Y
1         1  5328.172
2         2  7701.608
3         3 11248.606
4         4  9478.945
5         5  6138.471
6         6  8829.956
7         7  2838.390

Invoke the function:

> lr <- hanaml.LRSeasonalAdjust(data,
                                key = "TIMESTAMP",
                                endog = "Y",
                                forecast.length = 1,
                                trend = 0.9,
                                affect.future.only = TRUE,
                                seasonality = 2,
                                seasonal.handle.method = "lr",
                                accuracy.measure =  list("mape", "mpe"),
                                expost.flag = TRUE,
                                ignore.zero = TRUE)

Ouput:

> lr[[1]]$Collect()
  TIMESTAMP    VALUE
1         1 8472.320
2         2 8103.649
3         3 7734.978
4         4 7366.307
5         5 6997.636
6         6 6628.965
7         7 6260.294
8         8 5928.490

> lr[[2]]$Collect()
   STAT_NAME   STAT_VALUE
1  Intercept 8840.9904286
2      Slope -368.6708929
3       MAPE    0.3960495
4        MPE   -0.1719060
5 HandleZero    0.0000000