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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深度蜂窝循环网络，可有效分析具有空间信息的时间序列数据。

大规模时间序列数据的有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常规传感器信号处理管道通常涉及具有高维数据的巨大计算成本。深度递归神经网络已显示出在自动特征学习中改善时序处理的希望。但是，随着数据复杂性的提高，通用的深度递归模型在规模和深度上都在增长。在存在具有时间和空间特征的高维数据的情况下，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）体系结构，可以有效地处理具有空间信息的复杂多维时间序列数据。所提出模型中的蜂窝递归体系结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的同步处理。所提出的体系结构中由于蜂窝性而导致的广泛的可训练参数共享，确保了使用具有高维输入的循环处理单元的效率。这项研究还调查了提出的DCRNN模型用于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，使用两个时间序列数据集对提出的DCRNN体系结构进行了评估：用于癫痫发作检测的多通道头皮脑电图（EEG）数据集，以及在内部获得的机器故障检测数据集。