From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting
arXiv:2606.03097v1 Announce Type: new Abstract: Incorporating news into time series forecasting is appealing because news can reveal abrupt exogenous events that historical values alone cannot recover. However, existing LLM-based news-forecasting pipelines face two practical limitations: relevant news articles often exceed the model's context window, and iterative retrieval of supplementary news is typically unguided, leading to redundant updates and slow convergence. We address these issues with a novel framework that combines importance-aware news compression and process-level retrieval supe
The proliferation of real-time data and the increasing capabilities of large language models create an imperative to integrate complex, unstructured information like news into predictive analytics.
Improving time series forecasting through better news integration can enhance decision-making across finance, supply chains, and policy, providing a significant competitive edge.
Existing limitations in using LLMs for news-driven forecasting, specifically context window constraints and inefficient retrieval, are being directly addressed by new frameworks.
- · Financial Institutions
- · Supply Chain Management
- · Data-driven Enterprises
- · AI/ML Developers
- · Traditional Forecasting Models
- · Businesses reliant on lagging indicators
More accurate and timely predictions due to better incorporation of exogenous events.
Increased adoption of LLM-based forecasting across various industries, replacing simpler statistical models.
Enhanced resilience and agility for organizations able to anticipate black swan events or market shifts more effectively.
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Read at arXiv cs.AI