document: Semi-supervised time series classification The problem of time series classification has attracted great interest in the last decade . However current research assumes the existence of large amounts of labeled training data . In reality , such data may be very difficult or expensive to obtain . For example , it may require the time and expertise of cardiologists , space launch technicians , or other domain specialists . As in many other domains , there are often copious amounts of unlabeled data available . For example , the PhysioBank archive contains gigabytes of ECG data . In this work we propose a semi-supervised technique for building time series classifiers . While such algorithms are well known in text domains , we will show that special considerations must be made to make them both efficient and effective for the time series domain . We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms , handwritten documents , and video datasets . The experimental results demonstrate that our approach requires only a handful of labeled examples to construct accurate classifiers .
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