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English(EN) Model selection with proper scoring rules on data sets of time series

arXiv论文分析时间序列模型选择

作者 PulseAugur 编辑部 · [3 个来源] ·

一篇新发表在arXiv上的研究论文探讨了概率时间序列预测的模型选择技术。研究强调了不同的聚合方法，如平均得分、中位数得分和平均排名，可能由于得分分布的偏斜而导致结论不一致。研究表明，更大的测试集有助于这些选择标准的收敛，但对于较短的测试集，只有平均得分能够可靠地识别出真实模型。该论文使用包括M5竞赛数据在内的间歇性时间序列来说明这些发现，并强调了足够测试集大小的关键作用。 AI

影响 为提高时间序列预测模型选择的可靠性提供了见解，尤其是在数据有限的情况下。

排序理由 arXiv上发表的研究论文，详细介绍了时间序列模型选择的统计方法。

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arXiv论文分析时间序列模型选择

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arXiv上发表的研究论文，详细介绍了时间序列模型选择的统计方法。
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报道来源 [3]

  1. arXiv stat.ML TIER_1 English(EN) · Giorgio Corani, Stefano Damato, Dario Azzimonti, Lorenzo Zambon ·

    使用正确评分规则对时间序列数据集进行模型选择

    arXiv:2606.24715v1 Announce Type: new Abstract: We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule, we denote by the term \textit{score} the average value of the scoring rule on the test of an individu…

  2. arXiv stat.ML TIER_1 English(EN) · Lorenzo Zambon ·

    使用恰当评分规则对时间序列数据集进行模型选择

    We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule, we denote by the term \textit{score} the average value of the scoring rule on the test of an individual time series. For model selection, we need agg…

  3. arXiv stat.ML TIER_1 English(EN) · Lorenzo Zambon ·

    使用恰当评分规则在时间序列数据集上进行模型选择：倾向于平均缩放得分

    We study the problem of model selection among probabilistic forecasting models evaluated on datasets of multiple time series. The performance of a model on a single time series is quantified by the average value (score) of a proper scoring rule over a test set, but extending mode…

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