Abstract
Predicting stock price based on the features of raw data has been a significant but challenging task for researchers. Various frequency components of the raw stock price series represent characteristics of stock prices in different time scales. Therefore, it makes sense for predicting stock prices to take these frequency components into account. In this paper, a novel hybrid model is proposed to predict stock prices, which combines empirical mode decomposition (EMD), convolutional neural network (CNN) and Long Short-Term Memory (LSTM). For this purpose, the original stock price series are first decomposed into a finite number of intrinsic mode functions (IMFs) under different frequencies by EMD. For each component, a CNN is used to extract the features. Then through a LSTM network, the temporal dependencies of all extracted features are modeled and the final predicted prices are obtained after a linear transformation. Two prediction steps, one day and one week, of Shanghai Stock Exchange Composite Index (SSE) are used to test the proposed model. The experimental results show that the hybrid network can achieve better performances by modeling different frequencies compared with other state-of-the-art models.
Highlights
Stock price prediction is an important issue to stock investors to seek profit-maximization strategies [1]–[4]
Traditional artificial neural networks (ANN) lacks the ability to model the long-term dependency of time series, which promotes the proposing of Long Short-Term Memory (LSTM) network, a gated memory cell
It is significant to reveal the multi-frequency characteristics of the stock price timeseries. Both discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are effective tools to decompose time series and they have been applied in previous researches
Summary
Stock price prediction is an important issue to stock investors to seek profit-maximization strategies [1]–[4]. It is significant to reveal the multi-frequency characteristics of the stock price timeseries Both discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are effective tools to decompose time series and they have been applied in previous researches. Empirical mode decomposition (EMD), proposed by Huang et al [14], is a method for proceeding nonlinear and none-stationary time series, which decomposed original data into a finite number of intrinsic mode functions (IMFs) and a residue, which contain information about different frequencies. The proceedings of the researches are based on the assumption that the decomposed values at the current time are independent of future information They decompose full series at once and don’t take the effect of future prices on the decomposed components into account. The original data in this paper comes from samples of Shanghai Stock Exchange Composite Index (SSE) data
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