Nexus : An Agentic Framework for Time Series Forecasting
Summary
Nexus introduces a multi-agent framework that decomposes time series forecasting into specialized stages, integrating numerical patterns and contextual information using LLMs, achieving state-of-the-art results on benchmarks.
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Paper page - Nexus : An Agentic Framework for Time Series Forecasting
Source: https://huggingface.co/papers/2605.14389
Abstract
Nexus is a multi-agent forecasting framework that decomposes time series prediction into specialized stages, enabling effective integration of numerical patterns and contextual information for improved forecasting accuracy and explainability.
Time series forecasting is not just numerical extrapolation, but often requires reasoning with unstructured contextual data such as news or events. While specializedTime Series Foundation Models(TSFMs) excel at forecasting based on numerical patterns, they remain unaware to real-world textual signals. Conversely, while LLMs are emerging as zero-shot forecasters, their performance remains uneven across domains and contextual grounding. To bridge this gap, we introduce Nexus, amulti-agent forecastingframework that decomposes prediction into specialized stages: isolating macro-level and micro-leveltemporal fluctuations, and integratingcontextual informationwhen available before synthesizing a final forecast. This decomposition enables Nexus to adapt from seasonal signals to volatile, event-driven information without relying on external statistical anchors or monolithic prompting. We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized, depending critically on how numerical and contextual reasoning are organized. Evaluated on data strictly succeeding LLM knowledge cutoffs spanning Zillow real estate metrics and volatile stock market equities, Nexus consistently matches or outperforms state-of-the-art TSFMs and strong LLM baselines. Beyond numerical accuracy, Nexus produces high-qualityreasoning tracesthat explicitly show the fundamental drivers behind each forecast. Our results establish that real-world forecasting is an agentic reasoning problem extending well beyond onlysequence modeling.
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