MambaStock: Selective state space model for stock prediction

Zhuangwei Shi
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Abstract

The stock market plays a pivotal role in economic development, yet its intricate volatility poses challenges for investors. Consequently, research and accurate predictions of stock price movements are crucial for mitigating risks. Traditional time series models fall short in capturing nonlinearity, leading to unsatisfactory stock predictions. This limitation has spurred the widespread adoption of neural networks for stock prediction, owing to their robust nonlinear generalization capabilities. Recently, Mamba, a structured state space sequence model with a selection mechanism and scan module (S6), has emerged as a powerful tool in sequence modeling tasks. Leveraging this framework, this paper proposes a novel Mamba-based model for stock price prediction, named MambaStock. The proposed MambaStock model effectively mines historical stock market data to predict future stock prices without handcrafted features or extensive preprocessing procedures. Empirical studies on several stocks indicate that the MambaStock model outperforms previous methods, delivering highly accurate predictions. This enhanced accuracy can assist investors and institutions in making informed decisions, aiming to maximize returns while minimizing risks. This work underscores the value of Mamba in time-series forecasting. Source code is available at https://github.com/zshicode/MambaStock.
MambaStock:用于股票预测的选择性状态空间模型
股票市场在经济发展中起着举足轻重的作用,但其错综复杂的波动性给投资者带来了挑战。因此,研究并准确预测股价走势对于降低风险至关重要。传统的时间序列模型在捕捉非线性方面存在不足,导致股票预测结果不尽人意。由于神经网络具有强大的非线性泛化能力,这一局限性促使神经网络被广泛应用于股票预测。最近,具有选择机制和扫描模块(S6)的结构化状态空间序列模型 Mamba 成为序列建模任务中的有力工具。利用这一框架,本文提出了一种基于 Mamba 的新型股票价格预测模型,命名为 MambaStock。所提出的 MambaStock 模型可以有效地挖掘历史股票市场数据来预测未来股票价格,而无需手工制作特征或大量预处理程序。对几种股票的实证研究表明,MambaStock 模型优于以前的方法,能提供高精度的预测。这种更高的准确性可以帮助投资者和机构做出明智的决策,从而实现收益最大化和风险最小化。这项工作强调了 Mamba intime 系列预测的价值。源代码可在https://github.com/zshicode/MambaStock。
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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