# Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification

## TL;DR

This paper introduces a hybrid model combining gated recurrent units and convolutional networks for univariate time series classification, demonstrating improved accuracy, efficiency, and simplicity over LSTM-based models.

## Contribution

The paper proposes replacing LSTM with GRU in hybrid models, achieving better performance and efficiency in time series classification.

## Key findings

- GRU-FCN outperforms LSTM-FCN on multiple datasets.
- GRU-FCN has fewer parameters and faster training.
- The model simplifies hardware implementation.

## Abstract

Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better performance on many time series datasets. The proposed GRU-FCN model outperforms state-of-the-art classification performance in many univariate and multivariate time series datasets. In addition, since the GRU uses a simpler architecture than the LSTM, it has fewer training parameters, less training time, and a simpler hardware implementation, compared to the LSTM-based models.