OSTSC: Over Sampling for Time Series Classification in R

TL;DR

OSTSC is an R package that enhances time series classification by oversampling imbalanced data, significantly improving RNN classifier performance, especially in high-frequency trading datasets.

Contribution

This paper introduces OSTSC, a novel oversampling method for univariate, multinomial time series data in R, with demonstrated scalability and performance improvements.

Findings

OSTSC increases LSTM AUC from 0.543 to 0.784 on trading data.

The package improves classifier performance on imbalanced datasets.

OSTSC scales effectively to larger datasets.

Abstract

The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article 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.