Seasonality Test

hanaml.SeasonalDecompose is a R wrapper for SAP HANA PAL seasonality test.

hanaml.SeasonalDecompose(
  data,
  key = NULL,
  endog = NULL,
  alpha = NULL,
  thread.ratio = NULL,
  extrapolation = NULL,
  smooth.width = NULL,
  auxiliary.normalitytest = 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 The criterion for the autocorrelation coefficient. The value range is (0, 1). A larger value indicates stricter requirement for seasonality. Defaults to 0.2.
thread.ratio	double, optional Controls the proportion of available threads that can be used by this function. The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads. Values between 0 and 1 will use up to that percentage of available threads. Values outside the range from 0 to 1 are ignored, and the actual number of threads used is then be heuristically determined. Defaults to -1.
extrapolation	logical, optional Specifies whether to extrapolate the endpoints. Set to 1 when there’s an end-point issue. Defaults to FALSE.
smooth.width	integer, optional Specifies the width of the moving average applied to non-seasonal data. 0 indicates linear fitting to extract trends. Can not be larger than half of the data length. Defaults to 0.
auxiliary.normalitytest	logical, optional Specifies whether to use normality test to identify model types. Defaults to FALSE.

Value

Returns a list of DataFrame:

• DataFrame 1
  Statistics for time series, structured as follows:
  

  • STAT_NAME: includes type (additive or multiplicative), period (number of seasonality), acf (autocorrelation coefficient).
    STAT_VALUE:value of stats above.

• DataFrame 2
  Seasonal decomposition table, structured as follows::
  

  • ID: index/time stamp..

  • SEASONAL: seasonality component.

  • TREND: trend component.

  • RANDOM: white noise component.

Examples

Input DataFrame data:

> data$Collect()
   TIMESTAMP    Y
1          1   10
2          2    7
3          3   17
4          4   34
5          5    9
......
31        31   83
32        32   55
33        33  207
34        34   25
35        35    0
36        36    0

Invoke the function:

> sd <- hanaml.SeasonalDecompose(data,
                                 thread.ratio = 0.5,
                                 alpha = 0.2)

Output:

> sd[[1]]$Collect()
  STAT_NAME     STAT_VALUE
1      type multiplicative
2    period              4
3       acf       0.501947

> sd[[2]]$Collect()
   TIMESTAMP  SEASONAL   TREND    RANDOM
1          1 1.2526605  10.125 0.7884453
2          2 0.3499516  14.000 1.4287688
3          3 0.7488509  16.875 1.3452711
4          4 1.6485370  16.750 1.2313043
......
33        33 1.2526605  82.125 2.0121557
34        34 0.3499516  64.875 1.1011706
35        35 0.7488509  32.125 0.0000000
36        36 1.6485370   3.125 0.0000000