Stock transaction prediction modeling and analysis based on LSTM
Stock transaction prediction modeling and analysis based on LSTM
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基于LSTM的股票交易预测建模与分析
DOI：
10.1109/iciea.2018.8398183
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yifan Ding
中科院分区：
文献类型：
--
作者：
Siyuan Liu;Guangzhong Liao;Yifan Ding
股票价格波动是一个高度复杂的非线性动力系统。股票的成交量影响着股票的自相关性、自相关性和惯性效应，而股票的调整并不是以一个齐次的时间过程推进的，它有自己独立的时间推进过程。LSTM（Term Memory Long-Short）是一种时间递归神经网络，适用于时间序列中间隔和长延迟的重要事件的处理和预测。基于股票的时间特性和LSTM神经网络算法，利用LSTM递归神经网络对股票数据进行滤波、特征值提取和分析，建立相应股票交易的预测模型。
Stock price volatility is a highly complex nonlinear dynamic system. The stock's trading volume affects the stock's self correlation, self correlation and inertial effect, and the adjustment of the stock is not to advance with a homogeneous time process, which has its own independent time to promote the process. LSTM (Term Memory Long-Short) is a kind of time recurrent neural network, which is suitable for processing and predicting the important events of interval and long delay in time series. Based on temporal characteristics of stock and LSTM neural network algorithm, this paper uses the LSTM recurrent neural networks to filter, extract feature value and analyze the stock data, and set up the the prediction model of the corresponding stock transaction.