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首页> 外文会议>International Conference on Information Technology, Information System and Electrical Engineering >Golden Section Search-Multi Variable Algorithm for Optimization Parameter of Triple Exponential Smoothing Algorithm to Predict Sufferers of Lungs Disease
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Golden Section Search-Multi Variable Algorithm for Optimization Parameter of Triple Exponential Smoothing Algorithm to Predict Sufferers of Lungs Disease

机译：黄金分割搜索-多变量算法，优化三重指数平滑算法参数，预测肺癌患者

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摘要

Triple Exponential Smoothing is a prediction algorithm that considers time series data pattern and trends. The algorithm performance is getting better inline with the data volume. The specific feature of this algorithm is smoothing the previous period's data before performs a prediction task. However, this algorithm has weakness in setting the initial value of three parameters which are alfa, beta, and gamma, and it influences the prediction's accuracy. Therefore, we suggest Golden Section Search (GSS) optimization algorithm. The standard version of GSS optimizes a single variable. We modified GSS in order to optimize three parameters and called Golden Section Search-Multi Variable (GSS-MV). In order to optimize the parameters an extension of GSSMV with the triple exponential smoothing (called GSSMV-TES) will be proposed and tested in this paper. In order to evaluate the performance of the proposed algorithm a dataset of used the lungs' disease collected from a publicly available data source. In this research, two testing scenarios are designed. In the first scenario the data divided into training and testing in equal percentage (50% of each). In the second scenario, the data divided into 60% and 40% for training and testing respectively. According to the experiment results, GSSMV-TES shows better performance in the first scenario with the mean absolute percentage error (MAPE) at 3%. The mean percentage error of GSSMV-TES is observed 4% better than triple exponential smoothing (TES).
机译：三重指数平滑是一种考虑时间序列数据模式和趋势的预测算法。随着数据量的增加，算法性能越来越好。该算法的特定功能是在执行预测任务之前对前一时期的数据进行平滑处理。但是，该算法在设置阿尔法，贝塔和伽玛这三个参数的初始值方面存在弱点，并且会影响预测的准确性。因此，我们建议黄金分割搜索（GSS）优化算法。 GSS的标准版本可优化单个变量。我们修改了GSS以优化三个参数，并称为黄金分割搜索多变量（GSS-MV）。为了优化参数，本文提出并测试了具有三重指数平滑的GSSMV扩展（称为GSSMV-TES）。为了评估所提出算法的性能，从公众可获得的数据源收集了使用过的肺部疾病的数据集。在这项研究中，设计了两种测试方案。在第一种情况下，数据按相等的百分比（每种为50％）分为训练和测试。在第二种情况下，数据分别分为60％和40％用于训练和测试。根据实验结果，GSSMV-TES在第一种情况下显示出更好的性能，平均绝对百分比误差（MAPE）为3％。观察到的GSSMV-TES的平均百分比误差比三重指数平滑（TES）高4％。

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