README.md

In lweicdsor/OSTSC: Over Sampling for Time Series Classification

OSTSC

Over sampling for time series classification.

OSTSC implements oversampling for univariate, multimodal, time series classification. It has been tested in the Windows & Linux system.

Installation

library(devtools)
install_github("lweicdsor/OSTSC")

Usage

library(OSTSC)

Here is a simple example showing package usage. A tutorial with more complex examples is provided in the vignette. (https://github.com/lweicdsor/OSTSC/blob/master/inst/doc/Over_Sampling_for_Time_Series_Classification.pdf)

# loading data

data(Dataset_Synthetic_Control)

# get feature and label data

train.label <- Dataset_Synthetic_Control$train.y

train.sample <- Dataset_Synthetic_Control$train.x

# the first dimension of the feature set and labels should be the same

# the second dimension of feature set is the time sequence length

dim(train.sample)

dim(train.label)

# check the imbalance ratio of the labelled data

table(train.label)

# oversample the class 1 to the same amount as class 0

MyData <- OSTSC(train.sample, train.label)

# store the feature data after oversampling

x <- MyData$sample

# store the label data after oversampling

y <- MyData$label

# check the imbalance of the data

table(y)

lweicdsor/OSTSC documentation built on May 8, 2019, 1:13 p.m.