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arXiv paper analyzes model selection for time series forecasting

By PulseAugur Editorial · [3 sources] ·

A new research paper published on arXiv explores model selection techniques for probabilistic time series forecasting. The study highlights how different aggregation methods, such as mean score, median score, and mean rank, can lead to conflicting conclusions due to score distribution skewness. It demonstrates that larger test sets help these selection criteria converge, but for shorter test sets, only the mean score reliably identifies the true model. The paper illustrates these findings using intermittent time series, including data from the M5 competition, emphasizing the critical role of adequate test set size. AI

IMPACT Provides insights into improving the reliability of model selection for time series forecasting, particularly with limited data.

RANK_REASON Research paper published on arXiv detailing statistical methods for model selection in time series.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

arXiv paper analyzes model selection for time series forecasting

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Research paper published on arXiv detailing statistical methods for model selection in time series.
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COVERAGE [3]

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

    Model selection with proper scoring rules on data sets of time series

    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 ·

    Model selection with proper scoring rules on data sets of time series

    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 ·

    Model selection with proper scoring rules on data sets of time series: prefer the mean scaled score

    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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