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Seismic monitoring systems sift through seismograms in real-time, searching for target events, such as underground explosions. In this monitoring system, a burst of aftershocks (minor earthquakes occur after a major earthquake over days or even years) can be a source of confounding signals. Such a burst of aftershock signals can overload the human analysts of the monitoring system. To alleviate this burden at the onset of a sequence of events (e.g., aftershocks), a human analyst can label the first few of these events and start an online classifier to filter out subsequent aftershock events. We propose an online few-shot classification model FewSig for time series data for the above use case. The framework of FewSig consists of a selective model to identify the high-confidence positive events which are used for updating the models and a general classifier to label the remaining events. Our specific technique uses a %two-level decision tree selective model based on sliding DTW distance and a general classifier model based on distance metric learning with Neighborhood Component Analysis (NCA). The algorithm demonstrates surprising robustness when tested on univariate datasets from the UEA/UCR archive. Furthermore, we show two real-world earthquake events where the FewSig reduces the human effort in monitoring applications by filtering out the aftershock events.