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A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

机译:基于最小二乘支持向量回归和群体智能混合的时间序列预测新方法

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摘要

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction (FLSVR_(TSP)). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR's tuning parameters, the FLSVR_(TSP) incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the FLSVR_(TSP) has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.
机译:这项研究旨在建立一种新颖的混合人工智能(AI)方法,称为萤火虫调谐最小二乘支持向量回归,以进行时间序列预测(FLSVR_(TSP))。所提出的模型利用最小二乘支持向量回归(LS-SVR)作为监督学习技术来概括时间序列数据的输入和输出之间的映射函数。为了优化LS-SVR的调整参数,FLSVR_(TSP)结合了萤火虫算法(FA)作为搜索引擎。因此,新构建的模型可以从历史数据中学习并自动进行预测,而无需任何参数设置方面的先验知识。实验结果和比较结果表明,在预测人工和真实时间序列数据时,FLSVR_(TSP)在预测准确性上已取得了显着提高。因此,提出的混合方法是帮助决策者更好地应对时间序列预测的有前途的替代方法。

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  • 来源
    《Applied computational intelligence and soft computing》 |2014年第2014期|754809.1-754809.8|共8页
  • 作者单位

    Institute of Research and Development, Faculty of Civil Engineering, Duy Tan University, P809-K7/25 Quang Trung, Danang 59000, Vietnam;

    Faculty of Project Management, The University of Danang, University of Science and Technology, 54 Nguyen Luong Bang Danang 59000, Vietnam;

    Department of Construction Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Daan District, Taipei 8862, Taiwan;

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