论文标题
与时空信息转换的多任务GPR预测时间序列预测
Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation
论文作者
论文摘要
由于缺乏足够的信息，尤其是以多步骤的方式，很难从短期时间序列中准确预测未知系统。但是，高维短期时间序列包含丰富的动力学信息，并且在许多领域也越来越多。在这项工作中，通过利用时空信息（STI）转换方案，将这种高维/空间信息转换为时间信息，我们开发了一种称为MT-GPRMAchine的新方法，以从短期时间序列中实现准确的预测。具体而言，我们首先构建了一个特定的多任务GPR，它是多个链接的STI映射，以将高维/空间信息转换为任何给定目标变量的时间/动态信息，然后通过求解这些sti映射来对目标变量进行多步骤预测。对各种合成和现实数据集的多步预测结果清楚地验证了MT-Gprmachine优于其他现有方法。
Making an accurate prediction of an unknown system only from a short-term time series is difficult due to the lack of sufficient information, especially in a multi-step-ahead manner. However, a high-dimensional short-term time series contains rich dynamical information, and also becomes increasingly available in many fields. In this work, by exploiting spatiotemporal information (STI) transformation scheme that transforms such high-dimensional/spatial information to temporal information, we developed a new method called MT-GPRMachine to achieve accurate prediction from a short-term time series. Specifically, we first construct a specific multi-task GPR which is multiple linked STI mappings to transform high dimensional/spatial information into temporal/dynamical information of any given target variable, and then makes multi step-ahead prediction of the target variable by solving those STI mappings. The multi-step-ahead prediction results on various synthetic and real-world datasets clearly validated that MT-GPRMachine outperformed other existing approaches.
