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Hi, it's exciting to discover another remarkable work aimed at revitalizing RNN-based methods for time-series analysis tasks.
With the same goal in mind, we have introduced SegRNN (Paper link: https://arxiv.org/abs/2308.11200, Code link: https://github.com/lss-1138/SegRNN), demonstrating that RNNs still excel in time series forecasting tasks.
Specifically, SegRNN leverages two innovative strategies: Segment-wise Iterations and Parallel Multi-step Forecasting, enabling classical RNNs (e.g., GRU) to outperform state-of-the-art Transformer methods with significant efficiency advantages.
Once again, let's work together to make RNNs shine brightly.
The text was updated successfully, but these errors were encountered:
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Hi, it's exciting to discover another remarkable work aimed at revitalizing RNN-based methods for time-series analysis tasks.
With the same goal in mind, we have introduced SegRNN (Paper link: https://arxiv.org/abs/2308.11200, Code link: https://github.com/lss-1138/SegRNN), demonstrating that RNNs still excel in time series forecasting tasks.
Specifically, SegRNN leverages two innovative strategies: Segment-wise Iterations and Parallel Multi-step Forecasting, enabling classical RNNs (e.g., GRU) to outperform state-of-the-art Transformer methods with significant efficiency advantages.
Once again, let's work together to make RNNs shine brightly.
The text was updated successfully, but these errors were encountered: