A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands
A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands
复制标题
一种优化惰性学习参数以预测客户需求的遗传算法
DOI：
10.1109/icmla.2013.183
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
B. Scholz
中科院分区：
文献类型：
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作者：
Mirko Kück;B. Scholz
时间序列的预测无论在学术研究还是在工业应用中都是一个重要的课题。首先，必须选择合适的预测方法。随后，该预测方法的参数必须根据时间序列的演变进行调整。特别是，由于若干静态和动态影响，对未来客户需求的准确预测通常是困难的。作为一种很有前途的预测方法，我们提出了一种基于相空间重构和k-近邻搜索的懒惰学习算法。该算法源于混沌理论和非线性动力学。与广泛使用的线性预测方法（如Box-Jenkins ARIMA方法或指数平滑）相比，该方法适用于重建对时间序列数据的其他影响，并在预测中考虑这些影响。然而，为了调整预测方法的参数，以观察到的时间序列的演变，需要一个合理的优化算法。本文提出了一种求解参数优化问题的遗传算法。通过这种方式，预测方法自动准确且快速地拟合观察到的时间序列数据，以预测未来值。遗传算法的性能进行评估的应用程序，以不同的时间序列的客户需求在生产网络。结果表明，遗传算法是适当的，以找到合适的参数配置。此外，预测结果表明，所提出的预测算法相比线性标准方法的预测精度有所提高。
The prediction of time series is an important task both in academic research and in industrial applications. Firstly, an appropriate prediction method has to be chosen. Subsequently, the parameters of this prediction method have to be adjusted to the time series evolution. In particular, an accurate prediction of future customer demands is often difficult, due to several static and dynamic influences. As a promising prediction method, we propose a lazy learning algorithm based on phase space reconstruction and k-nearest neighbor search. This algorithm originates from chaos theory and nonlinear dynamics. In contrast to widely used linear prediction methods like the Box-Jenkins ARIMA method or exponential smoothing, this method is appropriate to reconstruct additional influences on the time series data and consider these influences within the prediction. However, in order to adjust the parameters of the prediction method to the observed time series evolution, a reasonable optimization algorithm is required. In this paper, we present a genetic algorithm for parameter optimization. In this way, the prediction method is automatically fitted accurately and quickly to observed time series data, in order to predict future values. The performance of the genetic algorithm is evaluated by an application to different time series of customer demands in production networks. The results show that the genetic algorithm is appropriate to find suitable parameter configurations. In addition, the prediction results indicate an improved forecasting accuracy of the proposed prediction algorithm compared to linear standard methods.