  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}
