Journal Article

A Sequence Prediction Algorithm Integrating Knowledge Graph Embedding and Dynamic Evolution Process

He, Jieguang, Qiu, Jinbo, Cui, Delong, Peng, Zhiping, Li, Qirui

2025

Overview

Sequence prediction is widely applied and has significant theoretical and practical application value in fields such as meteorology and medicine. Traditional models, such as LSTM(Long Short-Term Memory) and GRU(Gated Recurrent Unit), may perform better than this model when dealing with short-term dependencies, but their performance may decline on long sequences and complex data, especially in cases where sequence fluctuations are significant. However, the Transformer requires a large amount of computing resources (parallel computing) when dealing with long sequences. Aiming to solve the problems existing in sequence prediction models, such as insufficient modeling ability of long sequence dependencies, insufficient interpretability, and low efficiency of multi-element heterogeneous information fusion, this study embeds sequential data into the knowledge graph, enabling the model to associate context information when processing complex data and providing more reasonable decision support for the prediction results. Given the historical sequence and the predicted future sequence, three groups of sequence lengths were set in the experiment. And MAE (Mean Absolute Error)and MSE (Mean Square Error) are used as indicators for sequence prediction. In sequence prediction, dynamic evolution is conducive to enhancing the ability of the prediction model to capture the changing patterns of the current time series data and significantly improving the reliability of the prediction results. Experiments were conducted using five datasets from different application fields to verify the effectiveness of the prediction model. The experimental results show that based on the randomization of the prediction time step, the prediction model proposed in this study significantly improves the expression performance of stationary sequences. It has addressed the shortcomings of these traditional methods, such as maintaining good performance in the case of short sequences with large fluctuations.

• Publisher: MDPI AG
• Subject: Algorithms / Analysis / Data analysis / Data integration / Deep learning / Graphs / Knowledge representation / Methods / Prediction models / Recommender systems / Semantics / Sequences / Time series / Variables