Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information

TL;DR

This paper introduces a novel deep cellular recurrent neural network (DCRNN) architecture designed to efficiently analyze complex multi-dimensional time-series data with spatial information, achieving state-of-the-art results with fewer parameters.

Contribution

The paper presents a new cellular recurrent architecture that enables location-aware processing and parameter sharing for high-dimensional time-series data, improving efficiency and performance.

Findings

Achieves state-of-the-art classification accuracy on EEG seizure detection.

Uses significantly fewer trainable parameters than comparable models.

Demonstrates versatility across different application domains.

Abstract

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.