集装箱货运指数的新型智能预测模型:子序列自适应模型选择的新视角

IF 2.3 4区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Systems Pub Date : 2024-08-19 DOI:10.3390/systems12080309
Wendong Yang, Hao Zhang, Sibo Yang, Yan Hao
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引用次数: 0

摘要

集装箱货运指数的预测具有重要的经济和社会意义。以往的研究大多直接应用子预测器进行整合,无法针对不同的数据集进行优化。为填补这一研究空白,提高预测精度,本研究创新性地提出了一种基于自适应模型选择和多目标集合的集装箱货运指数预测新模型。所提出的模型包括以下四个模块:自适应数据预处理、模型库、自适应模型选择和多目标集合。具体来说,自适应数据预处理模块是基于一种新颖的模态分解技术建立的,该技术能有效降低历史数据扰动对预测模型的影响。其次,构建一个由四个基本预测因子组成的新模型库来预测子序列。然后,建立基于 Lasso 特征选择的自适应模型选择模块,为子序列选择有效的预测因子。对于子序列而言,不同的预测因子会产生不同的效果;因此,为了获得更好的预测结果,必须重新考虑每个预测因子的权重。因此,在多目标集合模块中引入了多目标人工秃鹫优化算法,可以有效提高预测模型的准确性和稳定性。此外,一个重要的发现是,所提出的模型可以获取不同的模型,随着不同数据集中提取数据特征的不同而自适应地变化,子序列选择多个模型或不选择模型的情况很常见。所提出的模型在实际货运市场中表现出了卓越的预测性能,在四个数据集中的平均 MAE、RMSE、MAPE、IA 和 TIC 值分别为 9.55567、11.29675、0.44222%、0.99787 和 0.00268。这些结果表明,所提出的模型具有出色的预测能力和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Intelligent Prediction Model for the Containerized Freight Index: A New Perspective of Adaptive Model Selection for Subseries
The prediction of the containerized freight index has important economic and social significance. Previous research has mostly applied sub-predictors directly for integration, which cannot be optimized for different datasets. To fill this research gap and improve prediction accuracy, this study innovatively proposes a new prediction model based on adaptive model selection and multi-objective ensemble to predict the containerized freight index. The proposed model comprises the following four modules: adaptive data preprocessing, model library, adaptive model selection, and multi-objective ensemble. Specifically, an adaptive data preprocessing module is established based on a novel modal decomposition technology that can effectively reduce the impact of perturbations in historical data on the prediction model. Second, a new model library is constructed to predict the subseries, consisting of four basic predictors. Then, the adaptive model selection module is established based on Lasso feature selection to choose valid predictors for subseries. For the subseries, different predictors can produce different effects; thus, to obtain better prediction results, the weights of each predictor must be reconsidered. Therefore, a multi-objective artificial vulture optimization algorithm is introduced into the multi-objective ensemble module, which can effectively improve the accuracy and stability of the prediction model. In addition, an important discovery is that the proposed model can acquire different models, adaptively varying with different extracted data features in various datasets, and it is common for multiple models or no model to be selected for the subseries.The proposed model demonstrates superior forecasting performance in the real freight market, achieving average MAE, RMSE, MAPE, IA, and TIC values of 9.55567, 11.29675, 0.44222%, 0.99787, and 0.00268, respectively, across four datasets. These results indicate that the proposed model has excellent predictive ability and robustness.
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来源期刊
Systems
Systems Decision Sciences-Information Systems and Management
CiteScore
2.80
自引率
15.80%
发文量
204
审稿时长
11 weeks
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