R: Change-point Detection

hanaml.CPD {hana.ml.r}

R Documentation

Change-point Detection

Description

hanaml.CPD is a R wrapper for PAL change-point detection algorithm.

Usage

hanaml.CPD(conn.context,
           data,
           key,
           features = NULL,
           cost = NULL,
           penalty = NULL,
           custom.pen = NULL,
           solver = NULL,
           lambda = NULL,
           min.size = NULL,
           min.sep = NULL,
           max.k = NULL,
           dispersion = NULL,
           lambda.range = NULL,
           max.iter = NULL)

Arguments

`conn.context`	`ConnectionContext` The connection to the SAP HANA system.
`data`	`DataFrame` Input data for change-point detection.
`key`	`character, optional` Column name for time-stamp of the input time-series data.
`features`	`character, optional` Column name(s) for the value(s) of the input time-series data.
`cost`	`c("normal.mse", "normal.rbf", "normal.mhlb", "normal.mv", "linear", "gamma", "poisson", "exponential", "normal.m", "negbinomial"), optional` The cost function for change-point detection. Defaults to 'normal_mse'.
`penalty`	`c("aic", "bic", "mbic", "oracle", "custom"), optional` The penalty function for change-point detection. Defaults to (1)"aic" if "solver" is "pruneddp", "pelt" or "opt", (2)"custom" if "solver " is "adppelt".
`custom.pen`	`numeric, optional` Specified the customized penalty value. Valid and mandatory only when penalty is explicitly set to "custom".
`solver`	`c("pelt", "opt", "adpelt", "pruneddp"), optional` Method for finding change-points of given data, cost and penalty. Each solver supports different cost and penalty functions. 1 For cost functions, "pelt", "opt" and "adpelt" support the following eight: "normal_mse", "normal_rbf", "normal_mhlb", "normal_mv", "linear", "gamma", "poisson", "exponential"; while "pruneddp" supports the following four cost functions: "poisson", "exponential", "normal_m", "negbinomial". 2 For penalty functions, "pruneddp" supports all penalties, "pelt", "opt" and "adppelt" support the following three: "aic","bic","custom", while "adppelt" only supports "custom" penalty. Defaults to "pelt".
`lambda`	`double, optional` Assigned weight of the penalty w.r.t. the cost function, i.e. penalizaion factor. It can be seen as trade-off between speed and accuracy of running the detection algorithm. A small values (usually less than 0.1) will dramatically improve the efficiency. Defaults to 0.02, and valid only when "solver" is "pelt" or "adppelt".
`min.size`	`integer, optional` The minimal length from the very begining within which change would not happen. Defaults to 2, valid only when "solver" is "opt", "pelt" or "adppelt".
`min.sep`	`integer, optional` The minimal length of speration between consecutive change-points. Defaults to 1, valid only when "solver" is "opt", "pelt" or "adppelt".
`max.k`	`integer, optional` The maximum number of change-points to be detected. If the given value is less than 1, this number would be determined automatically from the input data. Defaults to 0, vaild only when "solver" is "pruneddp".
`dispersion`	`double, optional` Dispersion coefficient for Gamma and negative binomial distribution. Defaults to 1.0, valid only when "cost" is "gamma" or "negbinomial".
`lambda.range`	`two numerical values, optional` Specifies the range for the weight of penalty functions, e.g. lambda.range <- c(0.01, 0.1) means the range of [0.01, 0.1]. Only valid when "solver" is "adppelt".
`max.iter`	`integer, optional` Maximum number of iterations for searching the best penalty. Valid only when "solver" is "adppelt". Defaults to 40.

Format

R6Class object.

Details

Change-point detection (CPDetection) methods aim at detecting multiple abrupt changes such as change in mean, variance or distribution in an observed time-series data.

Value

List of two DataFrames.
DataFrame 1: Detected change-points of the input time-series.
DataFrame 2: Statistics for running change-point detection on the input data.

Examples

## Not run: 
  >res <- hanaml.CPD(conn.context,solver="pelt",cost="normal.mse",penalty = "aic",lambda = 0.02)

## End(Not run)

[Package hana.ml.r version 1.0.8 Index]