Stock transaction prediction modeling and analysis based on LSTM

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.