Temporal classification prediction method and apparatus, server, and computer-readable storage medium

Abstract

Embodiments of the present application provide a time series classification prediction method and device, a server and a computer readable storage medium, relating to the technical field of machine learning. A time series feature sequence is input into a time series classification model for processing to obtain a time series classification result corresponding to each time point; the time series classification model obtains a model input vector by concatenating an original feature vector of each sample time series feature sequence at a time point and a feedback input vector at a previous time point, and inputs the model input vector into a pre-constructed initial time series classification model for training; the feedback input vector is selected from a real label and a model prediction value at the previous time point by a sampling probability corresponding to a current training round, the sampling probability representing a probability of taking the real label at the previous time point as the feedback input vector, and the sampling probability dynamically decays with an increase in the training round, so as to solve the exposure bias problem and improve the accuracy and stability of the time series classification result.

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