Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model; (2) parallelised neural networks to enable flexibility and avoid information overwhelming; and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
翻译:在文献中,多源时间序列的融合是通过使用各种学习模型实现的,这些模型忽视了特征内的时间模式和相关性,或者通过确定一个固定的窗口来选择数据集的具体部分。另一方面,许多研究表明在处理时间序列的不规则性方面有了重大改进,然而这些研究没有一项适用于多源数据。在这项工作中,我们设计了一个新颖的结构,即PIETS,以模型混杂的时间序列。 PIETS具有以下特点:(1) 多源样本的不规则化编码器,能够利用所有现有信息并加速模型的趋同;(2) 平行的神经网络,以便能够灵活和避免大量信息;(3) 关注机制,突出不同信息,并高度重视最相关的数据。通过对与COVID-19相关的真实世界数据集的广泛实验,我们表明拟议的结构能够有效地模拟不同时间数据并超越预测任务中的其他状态方法。