The research paper ss-Mamba: Semantic-Spline Selective State-Space Model introduces ss-Mamba, an advanced AI model that transforms how organizations forecast trends and patterns in data. Developed at National Chengchi University, it merges semantic understanding (language context), time-based adaptability, and efficient computation into one unified framework. Unlike traditional forecasting tools that focus only on numerical data, ss-Mamba “understands” the meaning and relationships behind time-series information, whether it’s sales, energy usage, or market signals, allowing for more accurate and interpretable predictions at lower cost. For business leaders, this breakthrough means faster, smarter forecasting systems that can scale across domains and operate efficiently on existing infrastructure, giving companies a strategic advantage in planning, risk management, and decision-making.

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Key point: This paper presents how the ss-Mamba model unites semantic understanding, adaptive time encoding, and efficient computation to deliver faster, more accurate, and interpretable AI-driven forecasting across diverse real-world domains.