Stock price prediction under multi-frequency model - based on attention state-frequency memory network

Wei Zhou, Yuting Pan, Zhaoxia Wu
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引用次数: 1

Abstract

Stock price forecasting is the most typical forecasting task in financial time series. However, stock prices are influenced by a variety of factors, with short-term and long-term trading activity being among the most important factors affecting price changes. The trading activity in these different frequency patterns is eventually reflected in the stock price data. Therefore, if the model identifies information about the potential multiple frequencies of the time series data in the process of prediction, it can enable the model to better learn the features of the time series data. In addition, stock price data are non-linear and non-stationary in nature, which makes it a challenging task to predict future trends. We used the attention state-frequency memory neural network (A-SFM) to accomplish the above objectives. The A-SFM realizes the decomposition of frequency by Fourier transform, and learns the state and frequency information by the powerful learning ability of neural network. In addition, we add an attention mechanism to the structure of the model to extract and learn the important parts of the price information. In this paper, we use the A-SFM model to capture the state and frequency information inherent in the series from past financial market data and use this information to make short- and long-term forecasts.
基于注意状态-频率记忆网络的多频模型下股价预测
股票价格预测是金融时间序列中最典型的预测任务。然而,股票价格受到多种因素的影响,短期和长期交易活动是影响价格变化的最重要因素之一。这些不同频率模式的交易活动最终反映在股票价格数据中。因此,如果模型在预测过程中识别出时间序列数据的潜在多频率信息,就可以使模型更好地学习时间序列数据的特征。此外,股票价格数据具有非线性和非平稳的性质,这使得预测未来趋势成为一项具有挑战性的任务。我们使用注意状态-频率记忆神经网络(A-SFM)来实现上述目标。A-SFM通过傅里叶变换实现频率分解,并利用神经网络强大的学习能力学习状态和频率信息。此外,我们在模型的结构中加入了关注机制来提取和学习价格信息的重要部分。在本文中,我们使用A-SFM模型从过去的金融市场数据中捕获序列中固有的状态和频率信息,并使用这些信息进行短期和长期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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