MambaStock: A Novel Model for Stock Price Prediction Using Mamba Framework

Khái niệm cốt lõi

The author introduces the MambaStock model, leveraging the Mamba framework, to predict future stock prices accurately by mining historical data without extensive preprocessing. The core strength lies in the model's ability to efficiently capture nonlinear patterns in sequential data.

Tóm tắt

The MambaStock model is proposed as a novel approach to stock price prediction, utilizing the Mamba framework. By effectively mining historical stock market data, this model outperforms traditional methods and assists investors in making informed decisions. Leveraging advanced deep learning architecture, MambaStock demonstrates superior performance in capturing complex patterns within stock market data.
Traditional time series models like ARIMA struggle with nonlinearity, while hybrid models like ARIMA-NN may be limited by inherent assumptions. In contrast, MambaStock excels at handling temporal dependencies and extracting relevant information effectively. The model's accuracy in predicting future stock prices highlights its potential for maximizing returns and minimizing risks.
Empirical studies on various stocks show that MambaStock outperforms baseline models such as XGBoost, LSTM, BiLSTM, Transformer, as well as hybrid models like TL-KF and AttCLX. The model's ability to handle complexity and nonlinearity sets it apart from traditional state space models and general-purpose machine learning models.

Thống kê

Empirical studies demonstrate that MambaStock has MSE of 1.1514 and RMSE of 1.0730.
The R2 value for MambaStock is 0.8873.
Results on different stocks show consistent superior performance of MambaStock over other methods.

Trích dẫn

"The enhanced accuracy of the MambaStock model can assist investors and institutions in making informed decisions."
"Mining historical stock market data without handcrafted features or extensive preprocessing procedures leads to highly accurate predictions."
"Mamba-based deep learning architecture extracts more relevant information than general-purpose machine learning models."

Thông tin chi tiết chính được chắt lọc từ

MambaStock

by Zhuangwei Sh... lúc arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18959.pdf

Yêu cầu sâu hơn

How does the adaptability of the Mamba framework contribute to its success in financial time series modeling

The adaptability of the Mamba framework plays a crucial role in its success in financial time series modeling. One key aspect is the selection mechanism that allows the model to dynamically choose relevant parts of the input sequence for making predictions. This adaptability enables Mamba to focus on important information while disregarding irrelevant or noisy data, leading to improved generalization and performance. Additionally, the scan module in Mamba operates by applying learnable parameters or operations to each window of the input sequence, capturing patterns and dependencies that span multiple time steps. This flexibility and adaptability allow Mamba to effectively capture complex patterns and relationships within financial data, making it well-suited for handling the intricate volatility present in stock market predictions.

What are the limitations of traditional time series models like ARIMA when applied to complex datasets like stock market predictions

Traditional time series models like ARIMA have limitations when applied to complex datasets such as stock market predictions. One major limitation is their inability to capture nonlinearity effectively. Stock market data often exhibits nonlinear patterns and behaviors that traditional linear models struggle to represent accurately, leading to unsatisfactory predictions. Moreover, ARIMA requires strict preconditions before modeling, which may not always hold true in real-world scenarios like stock markets where conditions are constantly changing and evolving rapidly. These limitations hinder traditional models from providing accurate forecasts in dynamic environments with intricate volatility like financial markets.

How can the principles from control theory and signal processing enhance sequence modeling tasks beyond financial markets

The principles from control theory and signal processing can significantly enhance sequence modeling tasks beyond financial markets by providing structured frameworks for efficient computation and storage of sequential data. By leveraging these principles, sequence modeling architectures can better capture underlying dynamics over time through state space representations. Structured matrices imposed on state spaces enable more effective training and inference processes while addressing challenges associated with traditional approaches.
Incorporating control theory concepts allows for dynamic adaptation based on system states, enhancing predictive capabilities across various domains beyond finance.
Signal processing techniques further contribute by enabling efficient extraction of relevant information from sequences through mechanisms like convolutions or recurrent connections within scan modules.
Overall, integrating principles from control theory and signal processing into sequence modeling tasks enhances model efficiency, accuracy, and adaptability across diverse applications beyond just financial markets.