Digital Library[ Search Result ]
Time Series Data Imbalance Resolution Techniques for Early Prediction
Eungseon An, Taehyoung Kwon, Doguk Kim
http://doi.org/10.5626/JOK.2025.52.7.593
Time series forecasting is a critical task that involves analyzing observed time series data to predict future values. However, when dealing with imbalanced data, model performance can degrade, leading to biased predictions. Although recent studies have explored various deep learning techniques and data augmentation methods, many fail to address challenges posed by data imbalance and the intrinsic characteristics of time series data simultaneously, leaving underlying issues unresolved. This study proposed a novel approach that could leverage temporal patterns to generate synthetic samples and extend the scope of early prediction. By identifying key moments that could effectively distinguish between positive and negative classes, our method enhanced the ability to predict further into the future. The method proposed in this study demonstrated superior performance to existing methods and proved the feasibility of early prediction for longer time lags.
A Study on Development of Technology to Improve Imbalanced Data Problems in Numerical Dataset Using Tomek Links Method combined with Balancing GAN
Hyunsik Na, Sohee Park, Daeseon Choi
http://doi.org/10.5626/JOK.2020.47.10.974
Machine Learning is useful due to its good performance and application in various fields such as data classification, voice recognition and predictive models. However, there exists a problem regarding the imbalance between classes in the training dataset, which degrades the classification performance of the minority class. In this paper, we propose a new data augmentation method that combines the Balancing GAN and Tomek Links Method to solve the Imbalanced Data problem and find a clear decision boundary. To verity the proposed method, we have evaluated the performance according to the classification model using five datasets. Moreover, the performance has been compared with Data Sampling and GAN based Data Augmentation Techniques. The results showed that the classification performance was improved or maintained by 0.05～0.195 in 17 of the total 25 performance evaluations. The method proposed in this paper showed the potential as a new method to solve the Imbalanced Data problem.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr