mlsanityData to 7 Oct 2026

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

arXiv 1812.07683 ↗First posted 18 Dec 2018cs.LG stat.MLcited by 26

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Abstract (arXiv)

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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2018.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 4; 2024: 5; 2025: 5; 2026: 3.

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