BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement
  for Transformers in Large-Scale Time Series Modeling
Authors: name: Li weile, name: Liu Xiao

Time series models face significant challenges in scaling to handle large and
complex datasets, akin to the scaling achieved by large language models (LLMs).
The unique characteristics of time series data and the computational demands of
model scaling necessitate innovative approaches. While researchers have
explored various architectures such as Transformers, LSTMs, and GRUs to address
these challenges, we propose a novel solution using RWKV-7, which incorporates
meta-learning into its state update mechanism. By integrating RWKV-7's time mix
and channel mix components into the transformer-based time series model Timer,
we achieve a substantial performance improvement of approximately 1.13 to 43.3x
and a 4.5x reduction in training time with 1/23 parameters, all while utilizing
fewer parameters. Our code and model weights are publicly available for further
research and development at https://github.com/Alic-Li/BlackGoose_Rimer.