Can decomposing forecasting into stages unlock numerical and contextual reasoning?

Time-series forecasting has had two roughly disjoint research traditions. Time-Series Foundation Models (TSFMs) excel at numerical extrapolation from historical patterns but are unaware of real-world textual signals — news, events, contextual catalysts. LLMs are emerging as zero-shot forecasters with the opposite profile: strong at contextual reasoning, uneven at numerical extrapolation. Each has what the other lacks.

Nexus bridges them through a multi-agent decomposition. The framework breaks forecasting into three stages. First, Contextualization: structure the raw multimodal context — historical data, news, events, domain signals — into a form the downstream agents can use. Second, Dual-Resolution Forecast Outlook Generation: produce projections at two temporal scales, isolating macro-level trajectories from micro-level event-driven catalysts. Third, Forecast Synthesis and Calibration: a Synthesizer Agent merges the dual-resolution outlooks with domain reasoning into a final calibrated forecast with explicit interpretable reasoning.

The architectural move is the dual resolution. Macro-level reasoning captures seasonal patterns, long-term trends, baseline trajectories — the regime where TSFM-style extrapolation works. Micro-level reasoning captures the event-driven catalysts and shocks — the regime where contextual LLM reasoning matters. By separating them and then synthesizing, the framework gets the strength of each without forcing a single model to handle both simultaneously.

The empirical result: on real-world Zillow real-estate and stock-market datasets, Nexus consistently outperforms both TimesFM-2.5 (a strong TSFM) and CoT LLM baselines. Decomposition into specialized stages adapts from seasonal signals to volatile event-driven information without relying on external statistical anchors or monolithic prompting.

For forecasting practitioners, this argues against pure-TSFM or pure-LLM approaches in domains where context matters. Real-estate prices respond to news (interest-rate decisions, regional events). Stock prices respond to announcements. Energy demand responds to weather and policy. These cases all benefit from architectures that explicitly separate the numerical and contextual contributions rather than asking one model to do both.