Abstract: In one embodiment, a system receives vehicle state data that includes directions and speeds of movement of a vehicle over a plurality of time periods, and determines a set of maneuvers performed by the vehicle over the plurality of time periods based on the vehicle state data. Each maneuver can represent a transition from a first state to a second state in a specified time period. The system also aggregates portions of the vehicle state data for each maneuver of the set of maneuvers to generate maneuver data, and determines maneuver characteristics based on the maneuver data, where the maneuver characteristics include motion information. The system further generates a safety score associated with a user of the vehicle based on the maneuver characteristics for each maneuver.

Abstract: Indications of interest can be automatically generated. For example, attributes of an item can be received. There can be little or no historical data for the item. Multiple time series associated with other items can also be received. A first set of classifiers can identify a subset of magnitude-pattern groups based on the attributes. A first ensemble methodology can select a final magnitude-pattern group for the item from among the subset of magnitude-pattern groups. A second set of classifiers can determine a subset of interest volumes based on the attributes. A second ensemble methodology can select a final interest volume for the item from among the subset of interest volumes. Interest data can be generated based on the final magnitude-pattern group and the final interest volume. The interest data can provide an initial indication of interest in the item over a future time period.