arXiv (All)
open access
CompEvo: Competition-Induced Evolution for Multi-Agent in News-Driven Time Series Forecasting
News-driven time series forecasting uses evolving textual events together with historical observations to predict future values, supporting applications such as market risk monitoring and resource scheduling. In multi-agent settings, two challenges still remain. The first is degeneration of thought,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting#1109368
- TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series#1062285
- TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series#1043112
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?#125401
- How Proper Scoring Rules Shape LLM Forecasting#997535
Record · ID 986968
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive