COMET-SG1 is a lightweight, stability-oriented autoregressive regression model designed for time-series prediction on edge and embedded AI systems. Unlike recurrent neural networks or transformer-based sequence models, COMET-SG1 operates through linear behavior-space encoding, memory-anchored transition estimation, and deterministic state updates. This structure prioritizes bounded long-horizon behavior under fully autoregressive inference, a critical requirement for edge deployment where prediction errors accumulate over time. Experiments on non-stationary synthetic time-series data demonstrate that COMET-SG1 achieves competitive short-horizon accuracy while exhibiting significantly reduced long-horizon drift compared to MLP, LSTM, and k-nearest neighbor baselines. With a compact parameter footprint and operations compatible with fixed-point arithmetic, COMET-SG1 provides a practical and interpretable approach for stable autoregressive prediction in edge and embedded AI applications.

翻译：COMET-SG1是一种轻量级、以稳定性为导向的自回归回归模型，专为边缘与嵌入式AI系统中的时间序列预测而设计。与循环神经网络或基于Transformer的序列模型不同，COMET-SG1通过线性行为空间编码、记忆锚定转移估计和确定性状态更新进行操作。该结构优先考虑完全自回归推理下有界的长期行为，这是边缘部署中预测误差随时间累积的关键要求。在非平稳合成时间序列数据上的实验表明，与MLP、LSTM和k近邻基线相比，COMET-SG1在实现有竞争力的短期预测精度的同时，表现出显著降低的长期漂移。凭借紧凑的参数占用和兼容定点算术的运算，COMET-SG1为边缘与嵌入式AI应用中的稳定自回归预测提供了一种实用且可解释的方法。