  Thanks to the rise of wearable and connected devices, sensor-generated time
series comprise a large and growing fraction of the world's data.
Unfortunately, extracting value from this data can be challenging, since
sensors report low-level signals (e.g., acceleration), not the high-level
events that are typically of interest (e.g., gestures). We introduce a
technique to bridge this gap by automatically extracting examples of real-world
events in low-level data, given only a rough estimate of when these events have
taken place.
  By identifying sets of features that repeat in the same temporal arrangement,
we isolate examples of such diverse events as human actions, power consumption
patterns, and spoken words with up to 96% precision and recall. Our method is
fast enough to run in real time and assumes only minimal knowledge of which
variables are relevant or the lengths of events. Our evaluation uses numerous
publicly available datasets and over 1 million samples of manually labeled
sensor data.
