Can decomposing forecasting into stages unlock numerical and contextual reasoning?
This explores whether breaking time-series forecasting into separate stages for contextualization, dual-resolution outlook, and synthesis allows systems to combine the strengths of numerical models and language models more effectively than either alone.
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.
Inquiring lines that read this note 20
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
What properties determine whether reward signals teach genuine reasoning? What structural advantages do diffusion language models offer over autoregressive methods? What determines success in training models on multiple tasks? What structural factors drive popularity bias in recommendation systems? How do evaluation biases undermine LLM quality assessment systems? Can prompting strategies overcome LLM biases without model fine-tuning? When does architectural design matter more than raw model capacity? How do training data properties shape reasoning capability development? Does decoupling planning from execution improve multi-step reasoning accuracy?- How much does workflow architecture matter compared to raw model capability in forecasting?
- Can modular expert decomposition extend beyond time into other causal dimensions?
Related concepts in this collection 3
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Can LLMs actually forecast time series better than we think?
Explores whether language models possess stronger forecasting ability than current benchmarks suggest, and what role workflow design plays in revealing or hiding that capability.
same paper, the broader observation
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Does separating planning from execution improve reasoning accuracy?
Can modular LM architectures that split problem decomposition from solution execution outperform monolithic models? This explores whether decoupling these cognitive operations reduces interference and boosts performance.
adjacent: same decomposition principle in a different domain
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Can reasoning systems maintain memory across retrieval cycles?
Existing retrieval systems treat each lookup independently. But what if reasoning required a persistent memory workspace that evolves as contradictions emerge and understanding deepens?
adjacent: another multi-stage decomposition for evidence-integrating reasoning
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Nexus: An Agentic Framework for Time Series Forecasting
- Approaching Human-Level Forecasting with Language Models
- A Decomposition Perspective to Long-context Reasoning for LLMs
- SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
- Can Large Language Models do Analytical Reasoning?
- Divide-or-Conquer? Which Part Should You Distill Your LLM?
- GenAI as a Power Persuader: How Professionals Get Persuasion Bombed When They Attempt to Validate LLMs
- Large Causal Models From Large Language Models
Original note title
multi-agent decomposition into contextualization dual-resolution outlook and synthesis stages enables time-series forecasting that integrates numerical patterns with unstructured contextual data