用检索增强实现零样本时间序列预测，提升准确率与效率。

Accurate forecasting of sequential data streams is a cornerstone of modern Web services, supporting applications such as traffic management, user behavior modeling, and online anomaly prevention. However, in many Web environments, new domains emerge rapidly and labeled history data is scarce, which makes zero-shot forecasting particularly challenging. Existing time-series pre-trained models (TSPMs) show promise but they lack the ability to dynamically incorporate external knowledge, while conventional retrieval-augmented generation (RAG) methods are rarely extended beyond text. In this work, we present \textbf{QuiZSF}, a retrieval-augmented forecasting framework that integrates search and forecasting for time series data. The framework performs search by retrieving structurally similar sequences from a large-scale time-series database, and it performs forecasting by integrating the retrieved knowledge into the target sequence. Specifically, QuiZSF introduces a \textbf{ChronoRAG Base}, a hierarchical tree-structured database that enables scalable and domain-aware retrieval, a \textbf{Multi-grained Series Interaction Learner} that captures fine- and coarse-grained dependencies between target and retrieved sequences, and a \textbf{Model Cooperation Coherer} that adapts retrieved knowledge to TSPMs. This design teaches models to actively perform search, align auxiliary information across modalities, and leverage it for more accurate forecasting. Extensive experiments on five public benchmarks demonstrate that QuiZSF consistently outperforms strong baselines, ranking first in up to \textbf{87.5\%} of zero-shot forecasting settings while maintaining high efficiency.