| 摘要 |
A predictive estimator, trained on a data corpus, is used to generate a probability estimate based a sequence of data related to an entity. The predictive estimator computes an instantaneous surprise score which is a quantification of a short-term deviation of a datum from the probability estimate. To compute the instantaneous surprise score, the predictive estimator is initialized with default values of the predictive estimator. Then, for each of data input of the datum to the predictive estimator, the instantaneous surprise score is calculated, corresponding to the deviation of the data input from the probability estimate. This generates an estimate of the probability of observing the datum given past data on the entity and the predictive estimator. The predictive estimator is updated with the datum and the time step advanced. |
