Event sequence prediction method based on time sequence convolution and relation modeling

Abstract

The invention discloses an event sequence prediction method based on time sequence convolution and relation modeling. The event sequence prediction method comprises the following steps: step 1, extracting an event sequence training set from a database; 2, preprocessing the original data; step 3, performing feature extraction on the mark information in the historical sequence by using a mark feature encoder; 4, performing feature extraction on the time information in the historical sequence by using a time sequence feature encoder; step 5; performing feature fusion on the mark feature codes and the time feature codes of the historical events, and outputting feature representation of a single historical event; step 6, constructing a time sequence correlation graph between events on the basis of the event codes, and outputting feature codes of a historical sequence; 7, respectively calculating condition intensity for each type of event; 8, calculating a model loss function and updating parameters; step 9, judging whether the loss curve of the model is converged or not, and if not, returning to step 8; and step 10, storing the trained model and performing deployment.

Description

technical field [0001] The invention relates to an event sequence prediction method, in particular to an event sequence prediction method based on time series convolution and relational modeling. Background technique [0002] The problem of event sequence prediction is an important research direction in the field of time series analysis. As early as the 1970s, some related scholars carried out related research work in this field. In our daily life, event sequence data is ubiquitous, and technical achievements related to event sequence prediction are applied in many scientific fields, such as social sciences, medicine, geology, and physics. It is of great social value to analyze and understand data related to event sequences and to make accurate predictions of future events, so this field has received extensive attention from the academic community. [0003] Conventional event sequence data is a set of sequences arranged according to the sequence of events. Each event sample...

AI Technical Summary

Problems solved by technology

[0006] Although the model based on the cyclic neural network already has a certain prediction accuracy, due to the characteristics of the cyclic neural network's own structure, it is difficult to perform parallel computing, so there is a problem of low computational efficiency in the process of model training and reasoning.

Moreover, although the cyclic neural network can encode and express sequence data, it cannot directly describe the influence relationship between different events in the sequence. Therefore, the correlation model not only lacks interpretability, but also does not fully understand the influence relationship between events. mining and analysis, limiting model performance

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