Abstract: A method, computer readable medium, and system are disclosed for identifying predicted labels for a plurality of time-associated data points. The time-associated data points may include data points ordered according to time, where each data point is associated with an individual time value. A convolutional neural network (CNN) is also trained, utilizing the predicted labels for the plurality of time-associated data points as well as ground truth labels for the plurality of time-associated data points, in order to create a trained CNN. The ground truth labels may include labels for the time-associated data points that have been verified as correct. Error correction is then performed, utilizing the trained CNN. For example, the trained CNN may create and return error-corrected labels for another plurality of time-associated data points, based on the predicted labels for the other plurality of time-associated data points, which may increase an accuracy of the labels.