R: AdditiveModelForecast

hanaml.AdditiveModelForecast {hana.ml.r}

R Documentation

AdditiveModelForecast

Description

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

Usage

hanaml.AdditiveModelForecast(conn.context,
                            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)

Arguments

`conn.context`	`ConnectionContext` The connection to the SAP HANA system.
`data`	`DataFrame` Input data. The structure is as follows. The first column: index (ID), timestamp. The second column: raw data. Other columns:: external 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, optional` Specify the regressor, include PRIOR.SCALE, STANDARDIZE, and MODE, it is json format \ such as ' "NAME": "X1", "PRIOR.SCALE":4, "MODE": "additive" '.
`changepoints`	`list of character, optional` Specify a list of changepoints in the format of timestamp, such as ['2019-01-01 00:00:00, '2019-02-04 00:00:00']
`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.
`weekly.seasonality`	`{'auto', 'false', 'true'}, optional` Specify whether or not to fit the weekly seasonality. 'auto' means automatically determined from input data.
`daily.seasonality`	`{'auto', 'false', 'true'}, optional` Specify whether or not to fit the daily seasonality. 'auto' means automatically determined from input data.
`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.

Format

R6Class object.

Details

Additive model forecast is an approach to forecast time series.

Value

Return a "AdditiveModelForecast" object with following values:

model : DataFrame
Fitted model.