title: BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement
    for Transformers in Large-Scale Time Series Modeling
summary: 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.
paper_authors._id: 67cfa436d37b8309603da1ee
paper_authors.name: Li weile
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-11T04:29:57.207Z
paper_authors._id: 66de61d7174e9c6971dbb253
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sM0xfS7HAkf_6GmkEGjDk.png
paper_authors.fullname: Alic Li
paper_authors.type: user
paper_authors.user: Alic-Li
paper_authors._id: 67cfa436d37b8309603da1ef
paper_authors.name: Liu Xiao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-11T03:33:06.087Z
paper_authors._id: 6176b32847ee6431f632981e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6176b32847ee6431f632981e/02rZ_oLAI0Ll6Y6be7Q9F.jpeg
paper_authors.fullname: IvanD
paper_authors.type: user
paper_authors.user: xiaol
paper_title: BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement
    for Transformers in Large-Scale Time Series Modeling
paper_summary: 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.
paper_ai_summary: A novel approach using RWKV-7 with meta-learning improves the performance of transformer-based time series models, achieving significant reductions in training time and parameter use.
paper_ai_keywords: Transformers LSTMs GRUs RWKV-7 meta-learning time mix channel mix Timer performance improvement training time parameter use