RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks

• URL: http://arxiv.org/abs/2401.09093v1
• Date: Wed, 17 Jan 2024 09:56:10 GMT
• Title: RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks
• Authors: Haowen Hou and F. Richard Yu
• Abstract summary: Traditional Recurrent Neural Network (RNN) architectures have historically held prominence in time series tasks. Recent advancements in time series forecasting have seen a shift away from RNNs to tasks such as Transformers, and CNNs. We design an efficient RNN-based model for time series tasks, named RWKV-TS, with three distinctive features.
• Score: 42.27646976600047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}

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