TS-DENSE: Time Series Data Augmentation by Subclass Clustering.

Classification of time series data is an essential task in applications of different fields, from Biology to Sports. Examples include human activity recognition, earthquake detection, insect species identification, and heartbeat analysis. The success of classification algorithms on these problems depends on having varied examples and a reasonable amount of labeled data for training an accurate predictive model. However, obtaining such data can be expensive or impossible in some scenarios, mainly when we collect data at a high sampling rate (e.g., 100 Hz), which is usual for sensors producing time series data. In this paper, we face the challenge of labeled examples scarcity for time series classification by employing data augmentation techniques to increase the number of training examples without collecting and labeling new data. Our method TS-DENSE is a data augmentation strategy based on subclass clustering and simple example modifications. In an evaluation with 48 benchmark datasets, TS-DENSE proved to be a consistent strategy improving the results in most of the problems and settings of the k-Nearest Neighbor algorithm, a state-of-the-art similarity-based method.