用两个指标提前判断时间序列能不能预测，帮决策者选对目标。
Time Series Forecastability Measures
- 通过频谱得分和李雅普诺夫指数评估数据本身可预测性。
- 在真实和合成数据上与模型实际表现强相关。
- 适合供应链、产品规划等需预判预测难度的场景。
本文提出两种度量时间序列可预测性的指标：频谱可预测性评分和最大李雅普诺夫指数。与传统模型评估指标不同，这些指标在建模前即可评估数据本身的可预测特性。频谱可预测性评分衡量时间序列中频率成分的强度与规律性，而李雅普诺夫指数则量化生成数据的系统混沌程度与稳定性。我们在M5预测竞赛数据集中的真实世界时间序列及合成数据上评估了这些指标的有效性。结果表明，这两个指标能准确反映时间序列的内在可预测性，并与多种模型的实际预测性能具有强相关性。通过在模型训练前理解时间序列的内在可预测性，从业者可将精力集中于更易预测的产品或供应链环节，同时对预测性差的项目设定合理预期或采用替代策略。
原文摘要 · Abstract (English)
This paper proposes using two metrics to quantify the forecastability of time series prior to model development: the spectral predictability score and the largest Lyapunov exponent. Unlike traditional model evaluation metrics, these measures assess the inherent forecastability characteristics of the data before any forecast attempts. The spectral predictability score evaluates the strength and regularity of frequency components in the time series, whereas the Lyapunov exponents quantify the chaos and stability of the system generating the data. We evaluated the effectiveness of these metrics on both synthetic and real-world time series from the M5 forecast competition dataset. Our results demonstrate that these two metrics can correctly reflect the inherent forecastability of a time series and have a strong correlation with the actual forecast performance of various models. By understanding the inherent forecastability of time series before model training, practitioners can focus their planning efforts on products and supply chain levels that are more forecastable, while setting appropriate expectations or seeking alternative strategies for products with limited forecastability.
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