  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.
