# Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets

arXiv: https://arxiv.org/abs/2110.04748 · HTML: https://mlsanity.com/paper/2110.04748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning](https://mlsanity.com/paper/1511.09337/) (2015)
- [Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification](https://mlsanity.com/paper/1801.04396/) (2018)

## Abstract (arXiv)

Time Series Classification (TSC) has drawn a lot of attention in literature because of its broad range of applications for different domains, such as medical data mining, weather forecasting. Although TSC algorithms are designed for balanced datasets, most real-life time series datasets are imbalanced. The Skewed distribution is a problem for time series classification both in distance-based and feature-based algorithms under the condition of poor class separability. To address the imbalance problem, both sampling-based and algorithmic approaches are used in this paper. Different methods significantly improve time series classification's performance on imbalanced datasets. Despite having a high imbalance ratio, the result showed that F score could be as high as 97.6% for the simulated TwoPatterns Dataset.