OSTSC: Over Sampling for Time Series Classification in R

33 Pages Posted: 30 Nov 2017

See all articles by Matthew Francis Dixon

Matthew Francis Dixon

Illinois Institute of Technology

Diego Klabjan

Northwestern University

Lan Wei

Illinois Institute of Technology

Date Written: November 26, 2017


The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This vignette provides a brief overview of the oversampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to quickly validate the functionality in the package. To demonstrate the performance impact of OSTSC, we then provide two medium size imbalanced time series datasets. Each example applies a TensorFlow implementation of a Long Short-Term Memory (LSTM) classifier - a type of a Recurrent Neural Network (RNN) classifier - to imbalanced time series. The classifier performance is compared with and without oversampling. Finally, larger versions of these two datasets are evaluated to demonstrate the scalability of the package. The examples demonstrate that the OSTSC package improves the performance of RNN classifiers applied to highly imbalanced time series data. In particular, OSTSC is observed to increase the AUC of LSTM from 0.543 to 0.784 on a high frequency trading dataset consisting of 30,000 time series observations.

Suggested Citation

Dixon, Matthew Francis and Klabjan, Diego and Wei, Lan, OSTSC: Over Sampling for Time Series Classification in R (November 26, 2017). Available at SSRN: https://ssrn.com/abstract=3077767 or http://dx.doi.org/10.2139/ssrn.3077767

Matthew Francis Dixon (Contact Author)

Illinois Institute of Technology ( email )

Department of Math
W 32nd St., E1 room 208, 10 S Wabash Ave, Chicago,
Chicago, IL 60616
United States

Diego Klabjan

Northwestern University ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Lan Wei

Illinois Institute of Technology ( email )

Stuart Graduate School of Business
565 W. Adams St.
Chicago, IL 60661
United States

Register to save articles to
your library


Paper statistics

Abstract Views
PlumX Metrics