Zhu Mingfu, Liu Yaxing, Qin Panke, Ding Yongjie, Cai Zhongqi, Gao Zhenlun, Ye Bo, Qi Haoran, Cheng Shenjie, Zeng Zeliang
Henan Polytechnic University, School of Computer Science and Technology, Jiaozuo, Henan, China.
Hebi National Optoelectronic Technology Co, Ltd., Hebi, Henan, China.
PeerJ Comput Sci. 2024 Dec 12;10:e2552. doi: 10.7717/peerj-cs.2552. eCollection 2024.
Long short-term memory (LSTM) networks, widely used for financial time series forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity and adaptability to market dynamics. Existing heuristic algorithms for LSTM often struggle to capture the complex dynamics of futures spread data, limiting prediction accuracy. We propose an integrated Cuckoo and Zebra Algorithms-optimised LSTM (ICS-LSTM) network for arbitrage spread prediction. This method replaces the Lévy flight in the Cuckoo algorithm with the Zebra algorithm search, improving convergence speed and solution optimization. Experimental results showed a mean absolute percentage error (MAPE) of 0.011, mean square error (MSE) of 3.326, mean absolute error (MAE) of 1.267, and coefficient of determination (R2) of 0.996. The proposed model improved performance by reducing MAPE by 8.3-50.0%, MSE by 10.2-77.8%, and MAE by 9.3-63.0% compared to existing methods. These improvements translate to more accurate spread predictions, enhancing arbitrage opportunities and trading strategy profitability.
长短期记忆（LSTM）网络广泛用于金融时间序列预测，但在套利价差预测中面临挑战，尤其是在针对大型数据集进行超参数调整时。这些问题影响了模型的复杂性和对市场动态的适应性。现有的LSTM启发式算法往往难以捕捉期货价差数据的复杂动态，限制了预测准确性。我们提出了一种用于套利价差预测的集成布谷鸟和斑马算法优化的LSTM（ICS-LSTM）网络。该方法用斑马算法搜索代替了布谷鸟算法中的莱维飞行，提高了收敛速度和解决方案优化。实验结果显示，平均绝对百分比误差（MAPE）为0.011，均方误差（MSE）为3.326，平均绝对误差（MAE）为1.267，决定系数（R2）为0.996。与现有方法相比，所提出的模型通过将MAPE降低8.3-50.0%、MSE降低10.2-77.8%和MAE降低9.3-63.0%来提高性能。这些改进转化为更准确的价差预测，增加了套利机会和交易策略的盈利能力。
