COMET-SG1: Lightweight Autoregressive Regressor for Edge and Embedded AI

• URL: http://arxiv.org/abs/2601.20772v1
• Date: Wed, 28 Jan 2026 16:59:56 GMT
• Title: COMET-SG1: Lightweight Autoregressive Regressor for Edge and Embedded AI
• Abstract summary: COMET-SG1 is a stability-oriented autoregressive regression model designed for time-series prediction on edge and embedded AI systems.<n>It operates through linear behavior-space encoding, memory-anchored transition estimation, and deterministic state updates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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