Efficient processing of large-scale time-series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand-engineered feature extraction often involve huge computational costs with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with the increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multidimensional time-series data with spatial information. The cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time-series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for the classification of multiclass time-series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series data sets: a multichannel scalp electroencephalogram (EEG) data set for seizure detection, and a machine fault detection data set obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

中文翻译：

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用于有效分析具有空间信息的时间序列数据的深度蜂窝循环网络


大规模时间序列数据的高效处理是机器学习中的一个复杂问题。具有手工设计的特征提取的传统传感器信号处理管道通常涉及高维数据的巨大计算成本。深度循环神经网络在自动化特征学习方面已显示出改善时间序列处理的前景。然而，随着数据复杂性的增加，通用深度循环模型的规模和深度都在增长。当存在具有时间和空间特征的高维数据时，这尤其具有挑战性。因此，这项工作提出了一种新颖的深度细胞递归神经网络（DCRNN）架构，可以有效地处理具有空间信息的复杂多维时间序列数据。所提出的模型中的蜂窝循环架构允许对来自空间分布的传感器信号源的时间序列数据进行位置感知同步处理。由于所提出的架构中的细胞性，广泛的可训练参数共享确保了具有高维输入的循环处理单元的使用效率。本研究还研究了所提出的 DCRNN 模型对来自不同应用领域的多类时间序列数据分类的多功能性。因此，所提出的 DCRNN 架构使用两个时间序列数据集进行评估：用于癫痫检测的多通道头皮脑电图（EEG）数据集和内部获得的机器故障检测数据集。结果表明，与文献中的类似方法相比，所提出的架构实现了最先进的性能，同时使用了更少的可训练参数。