RWKV-7 architecture offers a breakthrough approach for scaling time series models to handle complex, large-scale datasets with improved efficiency.

• Incorporates meta-learning into state updates, enabling better pattern recognition across temporal data
• Provides a simpler yet more effective replacement for traditional Transformer architectures
• Achieves superior scaling capabilities similar to those seen in large language models
• Addresses computational challenges in processing extensive time series datasets

This research significantly advances engineering capabilities for time series analysis, enabling more powerful predictive modeling and anomaly detection systems across industrial applications.

BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series Modeling