Time series prediction (TSP) is a process of using data collected at different times in the past for statistical analysis, so as to speculate on the trend of things, where the non-stationary and non-linear characteristics of data portray a hard setting for predictive tasks. Obviously, there will be no single model that could perform the best for all TSP issues. Dynamic Ensemble Selection (DES) technique achieves more accurate and robust performance than a single model, due to that it aims to select an ensemble of the most competent models in a dynamic fashion according to each test sample. A variety of DES approaches have been proposed to address pattern classification problems, but little work has been conducted on the research of TSP adopting the DES paradigm. Commonly, the DES approaches work by the definition of a single criterion to evaluate the capability of base classifiers. However, only one criterion is often inadequate for the comprehensive evaluation of classifier power. Thus, in this paper, a multiple criteria Dynamic Ensemble Pruning (DEP) technique exploiting meta-learning ad-hoc for TSP, termed DEP-TSP^meta, based on the inspiration from a state-of-the-art META-DES framework specifically presented for classification tasks, is developed. Within DEP-TSP^meta, Extreme Learning Machines (ELMs) and Hierarchical Extreme Learning Machines (H-ELMs) are integrated as the base models, and four distinct meta-attributes collections, i.e., hard prediction, local accuracy, global accuracy, and prediction confidence, are presented. Each set of meta-attributes corresponds to a specific assessment criterion, i.e., the prediction accuracy in local area of the eigenspace, the overall local accuracy, the prediction accuracy in global area of the decision space, and the confidence level of predictor. A desirable meta-predictor, obtained by training on the strength of these meta-attributes, is the key to deciding whether a base predictor is capable of predicting the unseen instance well or not. Those incapable base predictors determined by the meta-predictor will be pruned and the capable predictors will be expanded into the final dynamic ensemble system. The size of the sets of meta-attributes is specified dynamically by genetic algorithm for different time series benchmark datasets. Empirical results on eight benchmark datasets with different time granularities have verified that, the proposed DEP-TSP^meta algorithm possesses dramatically improved prediction performance at different granularities, when compared against three other DES approaches and four static selective ensemble learning methods.

时间序列预测（TSP）是使用过去不同时间收集的数据进行统计分析的过程，以便推测事物的趋势，其中数据的非平稳和非线性特征描绘了一个困难的环境。预测性任务。显然，将不会有任何一种模型可以在所有TSP问题上发挥最佳性能。动态集成选择（DES）技术比单个模型具有更准确和更强大的性能，因为它旨在根据每个测试样本以动态方式选择最有能力的模型集合。已经提出了各种各样的DES方法来解决模式分类问题，但是在采用DES范式的TSP的研究上却进行了很少的工作。通常，DES方法通过定义单个标准来评估基本分类器的功能。但是，只有一个标准通常不足以对分类器功效进行综合评估。因此，在本文中，采用了多准则动态集成修剪（DEP）技术基于专门针对分类任务而提供的最新META-DES框架的启发，开发了称为TSP的元学习临时工具，称为DEP-TSP ^meta。在DEP-TSP^元内，将极限学习机（ELM）和分层极限学习机（H-ELM）集成为基本模型，并提出了四个不同的元属性集合，即硬预测，局部精度，全局精度和预测置信度。每组元属性对应于一个特定的评估标准，即本征空间局部区域的预测精度，整体局部精度，决策空间全局区域的预测精度以及预测变量的置信度。通过训练这些元属性的强度而获得的理想元预测器是决定基本预测器是否能够很好地预测未见实例的关键。由元预测器确定的那些没有能力的基础预测器将被修剪，有能力的预测器将被扩展到最终的动态集成系统中。通过遗传算法为不同的时间序列基准数据集动态指定元属性集的大小。在八个具有不同时间粒度的基准数据集上的经验结果证明，提出的DEP-TSP与其他三种DES方法和四种静态选择性集成学习方法相比，^元算法在不同粒度下具有显着改善的预测性能。