用改进LSTM与跳跃扩散模型结合，提升原油等金融资产预测精度
Integrating LSTM Networks with Neural Levy Processes for Financial Forecasting
- 用灰狼优化器调优LSTM，结合麦顿-莱维跳跃扩散模型
- 在布伦特原油、泛欧600指数等三组数据上误差更低
- 适合需要高精度金融时序预测的研究者和量化团队
本文研究深度学习与金融模型融合以实现稳健的资产价格预测。提出一种混合框架，将长短期记忆网络（LSTM）与麦顿-莱维跳跃扩散模型相结合。为优化该框架，采用灰狼优化器（GWO）进行LSTM超参数调优，并探索三种麦顿-莱维模型参数校准方法：人工神经网络（ANN）、海豚捕食算法（MPA）及基于PyTorch的TorchSDE库。通过布伦特原油价格、STOXX 600指数和IT40指数三个真实金融数据集，对比了标准LSTM及结合分数赫斯顿模型的LSTM等基准模型。使用均方误差（MSE）、平均绝对误差（MAE）、均方百分比误差（MSPE）和决定系数（R²）评估性能。实验结果表明，经GWO优化的LSTM与由ANN校准的麦顿-莱维跳跃扩散模型结合的混合模型，在所有测试中优于基线模型。
原文摘要 · Abstract (English)
This paper investigates an optimal integration of deep learning with financial models for robust asset price forecasting. Specifically, we developed a hybrid framework combining a Long Short-Term Memory (LSTM) network with the Merton-Lévy jump-diffusion model. To optimise this framework, we employed the Grey Wolf Optimizer (GWO) for the LSTM hyperparameter tuning, and we explored three calibration methods for the Merton-Levy model parameters: Artificial Neural Networks (ANNs), the Marine Predators Algorithm (MPA), and the PyTorch-based TorchSDE library. To evaluate the predictive performance of our hybrid model, we compared it against several benchmark models, including a standard LSTM and an LSTM combined with the Fractional Heston model. This evaluation used three real-world financial datasets: Brent oil prices, the STOXX 600 index, and the IT40 index. Performance was assessed using standard metrics, including Mean Squared Error (MSE), Mean Absolute Error(MAE), Mean Squared Percentage Error (MSPE), and the coefficient of determination (R2). Our experimental results demonstrate that the hybrid model, combining a GWO-optimized LSTM network with the Levy-Merton Jump-Diffusion model calibrated using an ANN, outperformed the base LSTM model and all other models developed in this study.
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