Two models for predicting stock prices in combination with LSTM

Abstract

In addition to its practical and theoretical significance, stock forecasting has long been a hot research topic for scholars domestically and abroad. Stock data are time-series in nature, and neural networks have achieved relatively good performance in dealing with time series problems, among which long-short-term memory neural networks are well suited to dealing with such time-series data with long-term dependence. However, the stock market is an environment that changes with the external environment, with high stochasticity and complex intrinsic nonlinear relationships between different phenomena. Relying on a single method to identify the series directly cannot fully extract the complex information of the series changes, so the combined forecasting method is proposed. One idea is to combine empirical mode decomposition (EMD) with long-short-term memory (LSTM), and another idea is to incorporate LSTM or its variant GRU into generative adversarial networks (GAN/CGAN/WGAN). After an empirical study of Guanhao Bio's stock price and Apple's stock price, both methods present better prediction results.