Multivariate time series (MTS) prediction plays a key role in many fields such as finance, energy and transport, where each individual time series corresponds to the data collected from a certain data source, so-called channel. A typical pipeline of building an MTS prediction model (PM) consists of selecting a subset of channels among all available ones, extracting features from the selected channels, and building a PM based on the extracted features, where each component involves certain optimization tasks, i.e., selection of channels, feature extraction (FE) methods, and PMs as well as configuration of the selected FE method and PM. Accordingly, pursuing the best prediction performance corresponds to optimizing the pipeline by solving all of its involved optimization problems. This is a non-trivial task due to the vastness of the solution space. Different from most of the existing works which target at optimizing certain components of the pipeline, we propose a novel evolutionary ensemble learning framework to optimize the entire pipeline in a holistic manner. In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs). Finally, selective ensemble learning is designed to choose the optimal subset of solutions from the POSs and combine them to yield final prediction by using greedy sequential selection and least square methods. We implement the proposed framework and evaluate our implementation on two real-world applications, i.e., electricity consumption prediction and air quality prediction. The performance comparison with state-of-the-art techniques demonstrates the superiority of the proposed approach.
翻译:多重时间序列(MTS)的预测在许多领域发挥着关键作用,如金融、能源和运输等领域,每个单个时间序列都与从某个数据源,即所谓的频道收集的数据相对应。建立多边贸易体系预测模型(PM)的典型管道包括在所有可用渠道中选择一组渠道,从选定的渠道中提取特征,根据提取的特征建立一个总理职位,其中每个组成部分都涉及某些优化任务,即选择渠道、特征提取方法、管道以及选定FE方法和PM。因此,追求最佳预测性能匹配优化管道,解决所有涉及的优化应用问题。由于解决方案空间的广度,这是一个非三边性的任务。与大多数旨在优化管道某些组成部分的现有工程不同,我们提议了一个创新的全方位学习框架,以便以整体的方式优化整个管道。在这个框架内,一个特定的管道被编码为候选质量解决方案,一个多目的的进化算法,在不同的人口规模下,通过解决所有涉及优化应用的管道应用问题,优化管道。由于解决方案空间的广度,这是一个非三边际任务。与现有大多数旨在优化管道的某些内容不同,我们提出的进化混合学习整个管道的电路段选择。最后方法,我们提出的进化的电流和最终选择。我们所设计到最优化的ireabal- 和最终选择的电路段。我们所设计到最优化的平级选择的电路段。我们所设计到最优化的电路段。我们所设计的最佳选择的电路段。最后的电路段选择的电路段。最后的电路段选择的方法,通过。