A new accuracy measure based on bounded relative error for time series forecasting.
A new accuracy measure based on bounded relative error for time series forecasting.
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DOI：
10.1371/journal.pone.0174202
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Garibaldi JM
中科院分区：
文献类型：
--
作者：
Chen C;Twycross J;Garibaldi JM
过去，人们提出了许多时间序列预测比较的准确性度量。然而，这些措施中的许多遭受一个或多个问题，如对离群值和规模依赖性的抵抗力差。本文在总结常用精度测度的同时，对对称平均绝对百分比误差进行了专门的评述。此外，一个新的准确性措施，称为无标度的平均相对绝对误差（UMBRAE），它结合了各种替代措施的最佳功能，提出了解决现有措施的共同问题。对拟议的措施和相关措施进行了比较评价，既有合成数据，也有实际数据。结果表明，建议的措施，与用户可选择的基准，执行以及或优于其他措施上选定的标准。虽然它已被普遍接受，有没有一个最好的准确性措施，我们建议UMBRAE可能是一个很好的选择，以评估预测方法，特别是在几何平均相对误差，如几何平均相对绝对误差的基础上的措施的情况下，是首选。
Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred.