Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction

TL;DR

This paper introduces a new data augmentation method called Mean Gaussian Noise (MGN) to improve solar flare prediction by addressing class imbalance in multivariate time series data, demonstrating its effectiveness and efficiency.

Contribution

The study proposes MGN, a novel augmentation technique for multivariate time series, and evaluates its performance against existing methods in solar flare prediction.

Findings

MGN outperforms eight existing augmentation methods in classification accuracy.

MGN effectively mitigates class imbalance in solar flare datasets.

MGN has competitive computational cost compared to other methods.

Abstract

Time series data plays a crucial role across various domains, making it valuable for decision-making and predictive modeling. Machine learning (ML) and deep learning (DL) have shown promise in this regard, yet their performance hinges on data quality and quantity, often constrained by data scarcity and class imbalance, particularly for rare events like solar flares. Data augmentation techniques offer a potential solution to address these challenges, yet their effectiveness on multivariate time series datasets remains underexplored. In this study, we propose a novel data augmentation method for time series data named Mean Gaussian Noise (MGN). We investigate the performance of MGN compared to eight existing basic data augmentation methods on a multivariate time series dataset for solar flare prediction, SWAN-SF, using a ML algorithm for time series data, TimeSeriesSVC. The results demonstrate the efficacy of MGN and highlight its potential for improving classification performance in scenarios with extremely imbalanced data. Our time complexity analysis shows that MGN also has a competitive computational cost compared to the investigated alternative methods.