Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 512-516.doi: 10.11896/JsJkx.191100077

• Database & Big Data & Data Science • Previous Articles     Next Articles

Research on Premium Income Forecast Based on X12-LSTM Model

DIAO Li1 and WANG Ning2   

  1. 1 School of Insurance,Central University of Finance and Economics,BeiJing 100081,China
    2 School of Computer and Information Technology,BeiJing Jiaotong University,BeiJing 100044,China
  • Published:2020-07-07
  • About author:GRAVES A, JAITLYN, MOHAMED A R.Hybrid speech re-cognition with Deep Bidirectional LSTM//2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).IEEE, 2013.
    DIAO Li, born in 1992, Ph.D. Her main research interests include riskmanagement andinsurance.

Abstract: Under the new normal of economy,the prediction of premium income is a topic of common concern in academia and industry.Considering the strong seasonality of the time series data of premium income,an X12-LSTM model based on long short-term memory neural network is constructed to predict premium income,and compared with simple LSTM model,SARIMA model and BP neural network in this paper.Experimental results show that X12-LSTM model is the most accurate and stable model to predict premium income.Compared with simple LSTM model,the X12-LSTM model achieves an improvement of 8% in accuracy and 8% in stability,which shows that X12-lstm model is an effective improvement on simple LSTM model and is more suitable for data prediction with seasonality.

Key words: Long Short-Term Memoryneural networks, Premium Forecast, SARIMA, Seasonality, X12 seasonal adJustment

CLC Number: 

  • TP391
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