Additive Model Forecast

hanaml.AdditiveModelForecast is a R wrapper for SAP HANA PAL additive model forecast algorithm.

hanaml.AdditiveModelForecast(
  data = NULL,
  holiday = NULL,
  growth = NULL,
  logistic.growth.capacity = NULL,
  seasonality.mode = NULL,
  seasonality = NULL,
  num.changepoints = NULL,
  changepoint.range = NULL,
  regressor = NULL,
  changepoints = NULL,
  yearly.seasonality = NULL,
  weekly.seasonality = NULL,
  daily.seasonality = NULL,
  seasonality.prior.scale = NULL,
  holiday.prior.scale = NULL,
  changepoint.prior.scale = NULL,
  categorical.variable = NULL
)

Arguments

data

DataFrame
Input data. The structure is as follows.

  • The first column: index (ID), timestamp.

  • The second column: raw data.

  • Other columns:: other regressors' data.
holiday

DataFrame
Input holiday data. The structure is as follows.

  • The first column: index (ID), timestamp.

  • The second column: name, varchar.

  • The third column:: lower window of holiday, int.

  • The last column:: upper window of holiday, int.
growth

{"linear", "logistic"}, optional
Specify a trend, which could be either linear or logistic.
Defaults to "linear".
logistic.growth.capacity

numeric, optional
Specify the carrying capacity for logistic growth.
seasonality.mode

{"additive", "multiplicative"}, optional
Mode for seasonality, either additive or muliplicative.
seasonality

character, optional
Add seasonality to model, is a json format, include NAME, PERIOD, FOURIER.ORDER, PRIOR.SCALE,MODE,\ for example: ' "NAME": "MONTHLY", "PERIOD":30, "FOURIER.ORDER":5 '.
num.changepoints

integer, optional
Number of potential changepoints. Defaults to 25 if not provided.
changepoint.range

numeric, optional
Proportion of history in which trend changepoints will be estimated. Defaults to 0.8.
regressor

character or list of characters, optional
Specify the regressor, include PRIOR.SCALE, STANDARDIZE, and MODE, which is json format.
For example, regressor = ' "NAME": "X1", "PRIOR_SCALE":4, "MODE": "additive" '. Defaults to NULL.
changepoints

character or list of characters, optional
Specify a list of changepoints in the format of timestamp,
such as changepoints = list("2019-01-01 00:00:00", "2019-02-04 00:00:00"). Defaults to NULL.
yearly.seasonality

{"auto", "false", "true"}, optional
Specify whether or not to fit yearly seasonality.
"false" and "true" simply corresponds to their logical meaning, while "auto" means automatically determined from the input data. Defaults to "auto".
weekly.seasonality

{"auto", "false", "true"}, optional
Specify whether or not to fit the weekly seasonality.
"auto" means automatically determined from input data. Defaults to "auto".
daily.seasonality

{"auto", "false", "true"}, optional
Specify whether or not to fit the daily seasonality.
"auto" means automatically determined from input data. Defaults to "auto".
seasonality.prior.scale

numeric, optional
Parameter modulating the strength of the seasonality model. Defaults to 10 if not provided.
holiday.prior.scale

numeric, optional
Parameter modulating the strength of the holiday components model.
Defaults to 10 if not provided.
changepoint.prior.scale

numeric, optional
Parameter modulating the flexibility of the automatic changepoint selection.
Defaults to 0.05 if not provided.
categorical.variable

character or list/vector of characters, optional
Indicate which variables are treated as categorical. The default behavior is:

  • "VARCHAR" and "NVARCHAR": categorical.

  • "INTEGER" and "DOUBLE": continuous.

VALID only for variables of "INTEGER","VARCHAR" and "NVARCHAR" type, omitted otherwise.
No default value.

Value

Returns an "AdditiveModelForecast" object with following values:

  • model : DataFrame
    Fitted model.

Details

Additive Model Forecast use a decomposable time series model with three main components: trend, seasonality, and holidays or event.

Examples

Input DataFrame data:

> data$Collect()
   TIMESTAMP          Y
1 2007-12-11 -0.6361264
2 2007-12-12  3.0925087
3 2007-12-13 -0.7373356
4 2007-12-14 -3.1421910

Call the function:

> amf <- hanaml.AdditiveModelForecast(data = amf.df,
                                      yearly.seasonality = "auto",
                                      weekly.seasonality = "auto",
                                      daily.seasonality = "auto")

Output:

> amf$model$Collect()
ROW_INDEX
1         0
MODEL_CONTENT
1 {"FLOOR":0.0,"GROWTH":"linear","SEASONALITY_MODE":"additive","beta":[[0.0]],"changepoints_t":[[0.3333333333333333,0.6666666666666666]],"delta":[[3.606905203289095e-10,2.161407465026720e-10]],"holidays_prior_scale":10.0,"k":-0.692426758115090,"m":0.04386341358443890,"sigma_obs":0.5074218980767474,"start":"2007-12-11 00:00:00.0000000","t_scale":259200.0,"y_scale":3.1421909830}\n

See also

predict.AdditiveModelForecast