Traditional Recurrent Neural Network (RNN) architectures, such as LSTM and GRU, have historically held prominence in time series tasks. However, they have recently seen a decline in their dominant position across various time series tasks. As a result, recent advancements in time series forecasting have seen a notable shift away from RNNs towards alternative architectures such as Transformers, MLPs, and CNNs. To go beyond the limitations of traditional RNNs, we design an efficient RNN-based model for time series tasks, named RWKV-TS, with three distinctive features: (i) A novel RNN architecture characterized by $O(L)$ time complexity and memory usage. (ii) An enhanced ability to capture long-term sequence information compared to traditional RNNs. (iii) High computational efficiency coupled with the capacity to scale up effectively. Through extensive experimentation, our proposed RWKV-TS model demonstrates competitive performance when compared to state-of-the-art Transformer-based or CNN-based models. Notably, RWKV-TS exhibits not only comparable performance but also demonstrates reduced latency and memory utilization. The success of RWKV-TS encourages further exploration and innovation in leveraging RNN-based approaches within the domain of Time Series. The combination of competitive performance, low latency, and efficient memory usage positions RWKV-TS as a promising avenue for future research in time series tasks. Code is available at:\href{https://github.com/howard-hou/RWKV-TS}{ https://github.com/howard-hou/RWKV-TS}

翻译：传统循环神经网络（RNN）架构（如LSTM和GRU）曾在时间序列任务中占据主导地位。然而，近年来其在各类时间序列任务中的主导地位有所下降。受此影响，时间序列预测领域的最新发展已显著转向Transformer、MLP和CNN等替代架构。为突破传统RNN的局限性，我们设计了一种高效的基于RNN的时间序列模型——RWKV-TS，该模型具有三大特色：(i) 一种具备$O(L)$时间复杂度和内存占用的新型RNN架构；(ii) 相较于传统RNN，其对长序列信息的捕获能力显著增强；(iii) 兼具高计算效率与高效扩展能力。通过大量实验，我们所提出的RWKV-TS模型在与基于Transformer或CNN的最先进模型对比时展现出强劲的竞争力。值得注意的是，RWKV-TS不仅性能相当，还表现出更低的延迟和内存占用。RWKV-TS的成功鼓励我们进一步探索和利用基于RNN的方法在时间序列领域的创新。凭借其竞争性的性能、低延迟和高效内存使用，RWKV-TS为未来时间序列任务研究开辟了极具前景的路径。相关代码已公开于：\href{https://github.com/howard-hou/RWKV-TS}{https://github.com/howard-hou/RWKV-TS}