@Article{csse.2021.014189,
AUTHOR = {Guang Sun, Jingjing Lin, Chen Yang, Xiangyang Yin, Ziyu Li, Peng Guo,2, Junqi Sun, Xiaoping Fan, Bin Pan},
TITLE = {Stock Price Forecasting: An Echo State Network Approach},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {36},
YEAR = {2021},
NUMBER = {3},
PAGES = {509--520},
URL = {http://www.techscience.com/csse/v36n3/41260},
ISSN = {},
ABSTRACT = {Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.},
DOI = {10.32604/csse.2021.014189}
}