Time Series Active Learning using Automated Feature Extraction

Abstract: Time series classification is a prevalent problem for sensor data in an industrial setting. The ability to classify time series correctly allows for predictive maintenance which is the process of optimally maintaining assets and ensuring minimal ”down-time” of machines or processes. A requirement for classifying the time series are labeled time series that can be used as training instances. Labelling data is a time consuming and costly activity. Overcoming this, time series active learning attempts to label the most usefultime series in order for a machine learning model to learn quicker. Time series active learning is argued to be an understudied topic. Time series classification also faces the problem of large data dimensionality. By extracting features from the time series, one may reduce the dimensionality and leverage quicker processing. It is found that both suggested methods are faster and more accurate than a state-of-the-art method for time series active learning.