  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 cost 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 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 multi-dimensional 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 classification of
multi-class time series data from different application domains. Consequently,
the proposed DCRNN architecture is evaluated using two time-series datasets: a
multichannel scalp EEG dataset for seizure detection, and a machine fault
detection dataset 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.
