用动态组合模型提升股票预测精度，适配不同波动场景。
A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profiles
- 根据波动性自动切换RNN与线性模型，由门控网络决定权重。
- 在不同波动率下均降低均方误差（MSE）和平均绝对误差（MAE）。
- 适合需要自适应预测的金融建模场景，尤其对波动股表现更好。
本研究评估了混合专家（Mixture of Experts, MoE）模型在股票价格预测中的有效性，将其与循环神经网络（RNN）和线性回归模型进行比较。MoE框架通过门控网络动态分配权重，将RNN用于高波动性股票、线性模型用于低波动性股票。结果表明，该方法在不同波动性条件下显著提升预测准确性。RNN能有效捕捉高波动公司的非线性特征，但在稳定数据上易过拟合；线性模型则在趋势可预测时表现优异。MoE模型凭借自适应能力，整体优于单一模型，降低了均方误差（MSE）和平均绝对误差（MAE）。未来工作应优化门控机制，并在真实世界数据集上验证模型以增强实用性。
原文摘要 · Abstract (English)
This study evaluates the effectiveness of a Mixture of Experts (MoE) model for stock price prediction by comparing it to a Recurrent Neural Network (RNN) and a linear regression model. The MoE framework combines an RNN for volatile stocks and a linear model for stable stocks, dynamically adjusting the weight of each model through a gating network. Results indicate that the MoE approach significantly improves predictive accuracy across different volatility profiles. The RNN effectively captures non-linear patterns for volatile companies but tends to overfit stable data, whereas the linear model performs well for predictable trends. The MoE model's adaptability allows it to outperform each individual model, reducing errors such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Future work should focus on enhancing the gating mechanism and validating the model with real-world datasets to optimize its practical applicability.
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