Time series data event prediction method and system based on graph convolutional neural network and application thereof

Abstract

The invention discloses a time series data event prediction method and system based on a graph convolutional neural network and application of the time series data event prediction method and system,and the method comprises the steps: converting time series data after data cleaning into event sequence data at a preset time interval, and obtaining vector representation of an event and an event set; taking an event set contained in each piece of sequence sample data in the event sequence data at the last moment as a prediction target, and taking the prediction target as a corresponding sequencesample label to obtain labeled event sequence data; and when the graph convolutional neural network model is trained to meet a preset convergence condition, testing a model prediction effect by usingthe test set, and taking the model of the test effect as a final event prediction model. The method can make up for the defects that the traditional method has high requirements on data quantity andquality and cannot fully utilize the knowledge graph.

Description

technical field [0001] The invention belongs to the technical field of data mining and machine learning, relates to the field of time series data event prediction, in particular to a time series data event prediction method, system and application based on a graph convolutional neural network. Background technique [0002] Predicting possible future events based on historical time series data is an important research direction in the field of data mining and machine learning. The current main method is to mine the hidden patterns contained in time series data through traditional machine learning or deep learning technology, and use this information to calculate the probability of certain events in the future, so as to achieve the purpose of event prediction. [0003] Time-series data usually has the characteristics of time series, high dimensionality, and high noise. Using traditional machine technology to model time-series data requires domain experts to extract artificial ...

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Problems solved by technology

[0005] In summary, the current problems include: the deep learning model based on convolutional neural network is only suitable for one-dimensional or multi-dimensional grid data, and cannot effectively learn from the graph Key features are extracted from structural data; the way of introducing knowledge map information through the attention mechanism (Attention Mechanism) cannot fully utilize the spatial structure information of graph structured data, and a new time-series data event prediction method that can effectively use knowledge map information is urgently needed

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