TSrepr (version 1.0.4)

repr_featrend: FeaTrend representation of time series

Description

The repr_featrend computes representation of time series based on feature extraction from bit-level (trending) representation.

Usage

repr_featrend(x, func, pieces = 2L, order = 4L)

Arguments

x

the numeric vector (time series)

func

the function of aggregation, can be sumC or maxC or similar aggregation function

pieces

the number of parts of time series to split (default to 2)

order

the order of simple moving average (default to 4)

Value

the numeric vector of the length pieces

Details

FeaTrend is method of time series representation based on feature extraction from run lengths (RLE) of bit-level (trending) representation. It extracts number of features from trending representation based on number of pieces defined. From every piece, 2 features are extracted. You can define what feature will be extracted, recommended functions are max and sum. For example if max is selected, then maximum value of run lengths of ones and zeros are extracted.

See Also

repr_feaclip, repr_feacliptrend

Examples

# NOT RUN {
# default settings
repr_featrend(rnorm(50), maxC)

# compute FeaTrend for 4 pieces and make more smoothed ts by order = 8
repr_featrend(rnorm(50), sumC, 4, 8)

# }