Understanding and retrieving related real-world events based on their temporal dynamics is a fundamental challenge in time-sensitive applications such as forecasting, information retrieval, and social analysis. Existing methods often rely on semantic similarity or global time-series alignment, which overlook the transient and directional dependencies that frequently underlie real-world correlations. In this work, we introduce \textit{EventConnector}, a framework that constructs a temporal event graph capturing localized co-fluctuations and lead-lag relationships between events through their time-series trajectories. We further propose \textbf{EC-Fusion}, an adaptive retrieval mechanism that fuses EventConnector's graph-based scores with a complementary Granger-causal signal via a graph-quality-aware mixing weight. Across two real-world prediction market benchmarks (Polymarket and Kalshi) and nine forecasting architectures evaluated over three random seeds, EC-Fusion is the best non-oracle retrieval method on $17/18$ model--dataset cells, reducing RMSE by $6.87\%$ on average (up to $10.86\%$) over the strongest comparable retrieval baseline, with statistical significance at $p < 0.01$ after Holm--Bonferroni correction. These results highlight the effectiveness of temporally grounded graph modeling, augmented with causal-signal fusion, in capturing latent event relationships beyond what semantic similarity or traditional alignment techniques can offer.

翻译：基于事件时间动态理解与检索相关真实世界事件是时间敏感应用（如预测、信息检索和社交分析）中的基础挑战。现有方法通常依赖语义相似性或全局时间序列对齐，忽略了潜在现实世界关联中普遍存在的瞬时性和方向性依赖关系。本文提出**事件连接器**框架，通过构建时序事件图，利用事件的时间序列轨迹捕捉局部共波动和领先-滞后关系。我们进一步提出**EC-Fusion**自适应检索机制，通过图质量感知混合权重，将事件连接器基于图的评分与互补的格兰杰因果信号相融合。在Polymarket和Kalshi两个真实世界预测市场基准上，结合三个随机种子评估的九种预测架构中，EC-Fusion在$17/18$个模型-数据集组合中成为最优非先知检索方法，相较最强可比检索基线平均降低RMSE $6.87\%$（最高达$10.86\%$），经Holm-Bonferroni校正后统计显著性$p < 0.01$。这些结果凸显了基于时间图建模并融合因果信号增强，在捕捉超越语义相似性或传统对齐技术所能实现的潜在事件关系方面的有效性。