School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China.
Guizhou Key Laboratory of Big Data Statistics Analysis, Guizhou University of Finance and Economics, Guiyang, Guizhou, China.
PLoS One. 2023 Feb 2;18(2):e0280504. doi: 10.1371/journal.pone.0280504. eCollection 2023.
Shipping indices are extremely volatile, non-stationary, unstructured and non-linear, and more difficult to forecast than other common financial time series. Based on the idea of "decomposition-reconstruction-integration", this article puts forward a combined forecasting model CEEMD-PSO-BiLSTM for shipping index, which overcomes the linearity limitation of traditional models. CEEMD is used to decompose the original sequence into several IMF components and RES sequences, and the IMF components are recombined by reconstruction. Each sub-sequence is predicted and analyzed by PSO-BiLSTM neural network, and finally the predicted value of the original sequence is obtained by summing up the predicted values of each sub-sequence. Using six major shipping indices in China's shipping market such as FDI and BDI as test data, a systematic comparison test is conducted between the CEEMD-PSO-BiLSTM model and other mainstream time-series models in terms of forecasting effects. The results show that the model outperforms other models in all indicators, indicating its universality in different shipping markets. The research results of this article can deepen and improve the understanding of shipping indices, and also have important implications for risk management and decision management in the shipping market.
航运指数极具波动性、非平稳性、非结构性和非线性，比其他常见的金融时间序列更难预测。本文基于“分解-重构-集成”的思想，提出了一种航运指数的组合预测模型 CEEMD-PSO-BiLSTM，克服了传统模型的线性限制。CEEMD 用于将原始序列分解为几个 IMF 分量和 RES 序列，然后通过重构对 IMF 分量进行重新组合。通过 PSO-BiLSTM 神经网络对每个子序列进行预测和分析，最后通过汇总每个子序列的预测值得到原始序列的预测值。本文使用中国航运市场的 FDI 和 BDI 等六个主要航运指数作为测试数据，从预测效果方面对 CEEMD-PSO-BiLSTM 模型与其他主流时间序列模型进行了系统的对比测试。结果表明，该模型在所有指标上均优于其他模型，表明其在不同航运市场的通用性。本文的研究结果可以深化和提高对航运指数的认识，对航运市场的风险管理和决策管理也具有重要意义。
