Proceedings of the 3rd International Conference on Educational Innovation and Multimedia Technology, EIMT 2024, March 29–31, 2024, Wuhan, China

Research Article

Research on Prediction Algorithm of College Entrance Examination Filing Line Based on ARIMA and LSTM

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347733,
        author={Yifan  Yan and Zuxu  Dai},
        title={Research on Prediction Algorithm of College Entrance Examination Filing Line Based on ARIMA and LSTM},
        proceedings={Proceedings of the 3rd International Conference on Educational Innovation and Multimedia Technology, EIMT 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={EIMT},
        year={2024},
        month={6},
        keywords={filing line; arima model; lstm model; arima-lstm combined model},
        doi={10.4108/eai.29-3-2024.2347733}
    }
    
  • Yifan Yan
    Zuxu Dai
    Year: 2024
    Research on Prediction Algorithm of College Entrance Examination Filing Line Based on ARIMA and LSTM
    EIMT
    EAI
    DOI: 10.4108/eai.29-3-2024.2347733
Yifan Yan1,*, Zuxu Dai1
  • 1: Wuhan Institute of Technology
*Contact email: 1442094315@qq.com

Abstract

To improve the prediction accuracy of college entrance filing line, this study uses ARIMA-LSTM combined model to predict the rank of college entrance filing line based on score-to-rank conversion table. The model forecasts the rank of filing line for colleges, upon which the admission filing line is predicted. The ARIMA model is utilized to analyze linear relationships in the data, and its autoregressive coefficients set the time steps for the LSTM model, which addresses the nonlinear aspects of the forecast. The predictive results of the combined model are compared with those of the standalone ARIMA and LSTM models. The experimental results show that at the 90 % confidence level, the prediction error confidence interval of the ARIMA-LSTM combined model is (0.2, 3.6), which surpasses the ARIMA model's interval of (3.5, 6.6) and the LSTM model's interval of (-6.3, -2.7). This demonstrates the combined model's efficiency and accuracy in forecasting college entrance filing line.