Lbl - Lstm : Log Bilinear And Long Short Term Memory Based Efficient Stock Forecasting Model Considering External Fluctuating Factor

U. Gurav, D. S. Kotrappa
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引用次数: 2

Abstract

Stock market prediction problem is considered to be NP-hard problem because of highly volatile nature of stock market. In this paper, effort has been made to design efficient stock forecasting model using log Bilinear and long short term memory (LBL-LSTM) considering external fluctuating factor such as varying Bank's lending rates. The external factor bank's lending rates affects stock market performance ,as it plays vital role for the purchase of stocks in case of financial crisis faced by various business enterprises. Proposed LBL-LSTM based model shows performance improvement over existing machine learning algorithms used for stock market prediction.
Lbl - Lstm:考虑外部波动因素的对数双线性长短期记忆高效股票预测模型
由于股票市场的高度波动性,股票市场预测问题被认为是np困难问题。本文在考虑银行贷款利率变化等外部波动因素的基础上,设计了基于对数双线性长短期记忆(LBL-LSTM)的股票预测模型。外部因素银行的贷款利率影响着股票市场的表现,对于各类企业在面临金融危机的情况下购买股票起着至关重要的作用。提出的基于LBL-LSTM的模型比用于股票市场预测的现有机器学习算法的性能有所提高。
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