The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential. However, orchestrators face a severe "cold start" problem: newly discovered nodes lack the historical data required to train localized predictive models, while generalized models fail to capture unique hardware and microservice behaviors. To solve this, we propose a fully automated time-series prediction architecture driven by a novel data-mixing methodology. At the infrastructure level, we introduce a lightweight, technology-agnostic Resource Exposer (RE) that dynamically discovers nodes and continuously collects customizable telemetry (e.g., compute, network, energy). To overcome the sparsity of these initial local samples, our framework automatically merges them with TimeTrack, our publicly available, high-resolution dataset collected at 45-second intervals. This synergizes TimeTrack's foundational, high-frequency temporal patterns with the precise calibration of the local node data. Processed through a Neural Architecture Search (NAS) engine, the system automatically generates highly accurate baseline models. Experimental results demonstrate that merging the target data with TimeTrack effectively mitigates the cold start challenge. This integration significantly improves forecasting accuracy measured in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) and accelerates convergence compared to training on the sparse local samples alone, training solely on generic datasets, or mixing the target data with standard alternative datasets, establishing a robust foundation for continuous MLOps deployment.

翻译：云边连续体（Cloud-Edge Continuum, CEC）通过将资源分布到远边节点来支持延迟关键型应用，但其极端波动性使得基于时间序列预测的主动零接触管理变得至关重要。然而，编排器面临严重的“冷启动”问题：新发现的节点缺乏训练本地化预测模型所需的历史数据，而通用模型又无法捕捉独特的硬件和微服务行为。为解决这一问题，我们提出了一种全自动时间序列预测架构，其核心是一种新颖的数据混合方法。在基础设施层面，我们引入了一种轻量级、技术无关的资源暴露器（Resource Exposer, RE），可动态发现节点并持续采集可定制的遥测数据（例如计算、网络、能量）。为克服初始本地样本稀疏的问题，我们的框架自动将这些样本与TimeTrack（我们公开的高分辨率数据集，以45秒间隔采集）合并。这种合并协同利用了TimeTrack的高频基础时间模式与本地节点数据的精确校准特性。通过神经架构搜索（Neural Architecture Search, NAS）引擎处理，系统自动生成高精度的基线模型。实验结果表明，将目标数据与TimeTrack合并能有效缓解冷启动问题。与仅使用稀疏本地样本训练、仅基于通用数据集训练，或将目标数据与标准替代数据集混合相比，这种集成显著提升了以均方误差（MSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）衡量的预测精度，并加速了收敛，为持续MLOps部署奠定了坚实基础。