Knowledge discovery systems are nowadays supposed to store and process very large data. When working with big time series, multivariate prediction becomes more and more complicated because the use of all the variables does not allow to have the most accurate predictions and poses certain problems for classical prediction models. In this article, we present a scalable prediction process for large time series prediction, including a new algorithm for identifying time series predictors, which analyses the dependencies between time series using the mutual reinforcement principle between Hubs and Authorities of the Hits (Hyperlink-Induced Topic Search) algorithm. The proposed framework is evaluated on 3 real datasets. The results show that the best predictions are obtained using a very small number of predictors compared to the initial number of variables. The proposed feature selection algorithm shows promising results compared to widely known algorithms, such as the classic and the kernel principle component analysis, factor analysis, and the fast correlation-based filter method, and improves the prediction accuracy of many time series of the used datasets.

如今，知识发现系统应该存储和处理非常大的数据。当使用大时间序列时，多元预测变得越来越复杂，因为所有变量的使用都无法获得最准确的预测，并且对经典预测模型造成了某些问题。在本文中，我们提出了一种用于大时间序列预测的可扩展预测过程，其中包括一种用于识别时间序列预测变量的新算法，该算法使用集线器和点击数权威之间的相互加强原理来分析时间序列之间的依存关系（超链接诱导主题）搜索）算法。所提出的框架在3个真实数据集上进行了评估。结果表明，与初始变量数量相比，使用很少数量的预测变量可获得最佳预测。与经典算法和核主成分分析，因子分析以及基于快速相关性的滤波方法等广为人知的算法相比，所提出的特征选择算法显示出令人鼓舞的结果，并提高了所用数据集的多个时间序列的预测准确性。