Chronos比传统模型更适合长周期预测，且对调参要求低。
The Relevance of AWS Chronos: An Evaluation of Standard Methods for Time Series Forecasting with Limited Tuning
- 用Transformer框架的Chronos对比ARIMA和Prophet
- 长历史窗口下Chronos准确率更高，传统模型性能下降
- 不同用户类型表现差异大，适合真实场景部署
系统比较了基于Transformer的时间序列预测框架Chronos与ARIMA、Prophet等传统方法。在多个预测时长和用户类别上评估，重点关注历史上下文长度的影响。结果表明，Chronos在长期预测中表现更优，且随上下文增加仍保持高精度；而传统模型在上下文变长时性能显著下降。预测质量在不同用户类别间呈现系统性差异，说明底层行为模式始终影响模型表现。本研究支持在调参受限的真实场景中部署Chronos，尤其适用于需要长期预测的任务。
原文摘要 · Abstract (English)
A systematic comparison of Chronos, a transformer-based time series forecasting framework, against traditional approaches including ARIMA and Prophet. We evaluate these models across multiple time horizons and user categories, with a focus on the impact of historical context length. Our analysis reveals that while Chronos demonstrates superior performance for longer-term predictions and maintains accuracy with increased context, traditional models show significant degradation as context length increases. We find that prediction quality varies systematically between user classes, suggesting that underlying behavior patterns always influence model performance. This study provides a case for deploying Chronos in real-world applications where limited model tuning is feasible, especially in scenarios requiring longer prediction.
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