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粒度悖论：时间分解如何膨胀样本内拟合并加剧样本外误差The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
一篇题为《粒度悖论：时间分解如何膨胀样本内拟合并加剧样本外误差》的论文探讨了时间序列预测的复杂性。作者揭示，尽管更细的时间分解可以提高样本内拟合，但由于递归误差的累积，反而导致样本外准确性下降。这一发现强调了在评估不同粒度的预测模型时，特别是累积指标的模型评估指标需要谨慎考虑。
A recent paper titled 'The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error' explores the complexities of time-series forecasting. The authors reveal that while finer temporal disaggregation can lead to improved in-sample fit, it paradoxically results in poorer out-of-sample accuracy due to the compounding of recursive errors. This finding emphasizes the need for careful consideration of model evaluation metrics, particularly cumulative metrics, when assessing forecasting models across varying levels of granularity.
要点
更细的时间分解可以膨胀样本内拟合，但降低样本外性能。
Finer temporal disaggregation can inflate in-sample fit but degrade out-of-sample performance.
递归误差的累积是影响预测准确性的一个重要因素。
Recursive error compounding is a significant factor affecting forecasting accuracy.
累积指标对于评估不同粒度的模型性能至关重要。
Cumulative metrics are essential for evaluating model performance across different granularities.
为何重要
理解粒度悖论对于提高时间序列预测模型的可靠性至关重要，这些模型在各个行业的决策中被广泛使用。
Understanding the Granularity Paradox is crucial for improving the reliability of time-series forecasting models, which are widely used across various industries for decision-making.
产品经理
产品经理应意识到时间分解对预测准确性的影响，以便在产品特性和开发时间表上做出明智的决策。
Product managers should be aware of the implications of temporal disaggregation on forecasting accuracy to make informed decisions about product features and development timelines.
开发者
开发人员和工程师需要考虑递归误差对其预测模型的影响，并采用累积指标来增强模型评估和性能。
Developers and engineers need to consider the effects of recursive errors in their forecasting models and adopt cumulative metrics to enhance model evaluation and performance.
学生与求职者
对于学生和求职者，掌握这些概念可以在数据科学和分析等领域提供竞争优势，而这些领域对准确预测至关重要。
For students and job-seekers, grasping these concepts can provide a competitive edge in fields like data science and analytics, where accurate forecasting is critical.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.LG
The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample ErrorThe paper investigates the 'Granularity Paradox' in time-series forecasting, revealing that finer temporal disaggregation improves in-sample fit but worsens out-of-sample accuracy due to recursive error compounding. Through benchmarking various forecasting models, it highlights the importance of cumulative metrics in evaluating model performance across different granularities.