Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecasting
PositiveArtificial Intelligence
- Recent empirical studies have explored the capabilities of slow-thinking large language models (LLMs) like DeepSeek-R1 and ChatGPT-o1 in time series forecasting (TSF), proposing a new framework called TimeReasoner that treats TSF as a conditional reasoning task. This approach aims to enhance the models' ability to reason over temporal patterns, potentially improving forecasting accuracy even in zero-shot scenarios.
- The development of TimeReasoner is significant as it represents a shift from traditional fast-thinking paradigms in AI, which often prioritize quick pattern recognition over deeper reasoning. By leveraging the multi-step reasoning capabilities of slow-thinking LLMs, this research could lead to more reliable forecasting methods that incorporate temporal dynamics and contextual dependencies.
- This advancement aligns with ongoing discussions in the AI community regarding the balance between reasoning capabilities and foundational skills in LLMs. As models like DeepSeek-R1 demonstrate improved reasoning through techniques such as reinforcement learning and structured pruning, the challenge remains to mitigate issues like overthinking and hallucinations, which can undermine the reliability of AI outputs in critical applications.
— via World Pulse Now AI Editorial System
