Multi-agents-based news-driven time series forecasting is considered as a potential paradigm shift in the era of large language models (LLMs). The challenge of this task lies in measuring the influences of different news events towards the fluctuations of time series. This requires agents to possess stronger abilities of innovative thinking and the identifying misleading logic. However, the existing multi-agent discussion framework has limited enhancement on time series prediction in terms of optimizing these two capabilities. Inspired by the role of competition in fostering innovation, this study embeds a competition mechanism within the multi-agent discussion to enhance agents' capability of generating innovative thoughts. Furthermore, to bolster the model's proficiency in identifying misleading information, we incorporate a fine-tuned small-scale LLM model within the reflective stage, offering auxiliary decision-making support. Experimental results confirm that the competition can boost agents' capacity for innovative thinking, which can significantly improve the performances of time series prediction. Similar to the findings of social science, the intensity of competition within this framework can influence the performances of agents, providing a new perspective for studying LLMs-based multi-agent systems.

本研究针对现有多代理框架在时间序列预测中缺乏对于创新思维和识别误导性逻辑能力的提升进行了改进。通过引入竞争机制，本研究增强了代理生成创新思维的能力，并在反思阶段结合细调的小型语言模型以支持决策。实验结果表明，竞争不仅能提高代理的创新思维能力，还显著改善时间序列预测的表现，为基于大语言模型的多代理系统研究提供了新视角。

竞争能否提升大语言模型驱动代理在新闻驱动时间序列预测中的能力？