Large language models (LLMs) have shown strong multimodal reasoning abilities, yet their understanding of purely numerical time-series remains limited. Existing approaches mainly focus on forecasting or trend description, providing little insight into the underlying events that have occurred or are unfolding within observed numerical dynamics. We study number-to-event reasoning and decoding, a task that infers interpretable and structured event hypotheses directly from numerical time-series when explicit event information is unavailable at inference time. The task focuses on aligned and plausible event semantics that help explain observed numerical dynamics. To address data scarcity and semantic alignment challenges, we propose a reasoning-aware framework that combines an agent-guided event extractor, a Hawkes-process-based synthetic data generator, and a multi-stage fine-tuning pipeline integrating a time-series encoder with a structured event decoder. The model generates intermediate reasoning steps and outputs constrained event representations. Experiments across multiple real-world domains show that our approach consistently outperforms strong LLM baselines in event-level precision and recall, demonstrating its effectiveness in bridging numerical reasoning and semantic event understanding. The code is available at https://github.com/feng1201/NUM2EVENT_IDEA