Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets
This addresses the issue of skewed data distributions in time series classification for domains like medical data mining and weather forecasting, but it is incremental as it applies existing imbalance-handling methods to time series data.
The paper tackled the problem of time series classification on imbalanced datasets, which is common in real-life applications, by using sampling-based and algorithmic approaches, achieving an F-score of up to 97.6% on a simulated dataset with high imbalance.
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.