RTL: A Robust Time Series Labeling Algorithm

Abstract

Time series classification is one of the most important problems in data mining. With the growth in availability of time series data, many novel classification algorithms have been proposed. Despite the promising progress in accuracy, the performance of many algorithms still strongly depends on an initial training session containing labeled examples of all classes to be learned. In most realistic applications, however, labels are lacking or only partially available; limiting the practical applicability of time series classification algorithms with this requirement. To remedy this, we introduce the Robust Time series Labeling (RTL) algorithm and show its ability to increase labeling accuracy and robustness across a wide variety of time series datasets. Given its flexibility, the RTL algorithm can successfully be applied in many real-life situations.