Forecasting systems are commonly refreshed at every review period, and that refresh usually bundles two distinct operations: estimating parameters and selecting the model form. Recent evidence suggests the second operation is often unnecessary, since intermediate updating strategies can hold forecast accuracy roughly fixed while cutting computational cost and forecast instability. This technical note takes up the complementary question. Once a system has adopted a reduced-update policy, when should it interrupt that policy and re-specify the model form? We define specification debt as the evidence accumulated against the deployed model form, and we use it to build a cost-sensitive trigger for re-specification. In a closed discrete model space the trigger reduces to a threshold on the negative log posterior probability of the deployed specification. In open production settings the same decision rule can be run with predictive score gaps, stacking weights, or calibrated monitoring diagnostics. Fixed update frequencies turn out to be a special case of the rule, recovered when evidence against the deployed form accumulates at a constant rate. We illustrate the idea on 500 monthly M4 series, comparing full updating, fixed model-form update frequencies, parameter-only updating, and capped adaptive score-triggered updating, and within the finite ETS grid we also compute information-criterion analogues of specification debt from AIC and BIC weights over the candidate forms. In that illustration the best capped adaptive policy is comparable to full updating in accuracy, runs in about 28 percent of full-update computational time, lowers forecast instability, and behaves like a fixed schedule with a small number of evidence-based exceptions.

翻译：预测系统通常在每个评估周期进行更新，且该更新通常包含两个不同的操作：参数估计和模型形式选择。近期证据表明，第二个操作往往并非必要，因为中间更新策略可在保持预测准确性大致不变的同时，降低计算成本与预测不稳定性。本技术笔记探讨一个互补性问题：当系统已采用缩减更新策略后，应何时中断该策略并重新设定模型形式？我们将部署模型形式累积的反面证据定义为“设定债务”，并据此构建一个成本敏感的重新设定触发器。在封闭离散模型空间中，该触发器简化为对所部署模型形式负对数后验概率的阈值判定。在开放生产环境中，同一决策规则可通过预测得分差距、堆叠权重或校准监控诊断指标执行。固定更新频率实为该规则的一种特例——当反面证据以恒定速率累积时即可复现。我们基于500个M4月度序列进行验证，对比了完整更新、固定模型形式更新频率、仅参数更新以及受限自适应得分触发更新，并在有限ETS网格内，依据候选形式的AIC与BIC权重计算了信息准则形式的设定债务。在该验证中，最优受限自适应策略的准确性接近完整更新，运行时间约为完整更新的28%，降低了预测不稳定性，且表现为一种带有少量基于证据的例外的固定调度方案。