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The paper proposes MAPS, a model-agnostic score for estimating how predictable a multivariable time series forecasting task is, separate from how well any particular model performs. That separation matters for benchmarking, since current leaderboards conflate model quality with dataset difficulty. The reported r=0.963 agreement is on synthetic data with a known reference, so real-world reliability across datasets is the claim to test.
Model-agnostic predictability quantification of multivariable time series
arXiv:2610.33209v1 Announce Type: new Abstract: Multivariable time series forecasting plays an important role in various domains, where predictions depend on complex interactions among covariates. Forecasting models are commonly evaluated across numerous benchmark datasets. However, forecasting accuracy reflects both model capability and task predictability, making it difficult to characterise the difficulty of a task independently of the model used. Existing predictability measures often fail to capture nonlinear dependencies or are limited to univariate series. In this work, we propose Multivariable Predictability Score (MAPS), a model-agnostic metric for measuring the intrinsic predictability of multivariable time series forecasting tasks. MAPS uses covariate information to assess predictability across different lookback windows and forecasting horizons, and provides a quantitative assessment of the predictive information available for forecasting. We evaluate MAPS across synthetic and real-world forecasting datasets against 6 baseline predictability measures. The former confirms that our method closely aligns with the expected reference predictability (r=0.963). Empirical results further corroborate that MAPS yields more reliable projections in the majority of experimental configurations.
Abstract:Multivariable time series forecasting plays an important role in various domains, where predictions depend on complex interactions among covariates. Forecasting models are commonly evaluated across numerous benchmark datasets. However, forecasting accuracy reflects both model capability and task predictability, making it difficult to characterise the difficulty of a task independently of the model used. Existing predictability measures often fail to capture nonlinear dependencies or are limited to univariate series. In this work, we propose Multivariable Predictability Score (MAPS), a model-agnostic metric for measuring the intrinsic predictability of multivariable time series forecasting tasks. MAPS uses covariate information to assess predictability across different lookback windows and forecasting horizons, and provides a quantitative assessment of the predictive information available for forecasting. We evaluate MAPS across synthetic and real-world forecasting datasets against 6 baseline predictability measures. The former confirms that our method closely aligns with the expected reference predictability (r=0.963). Empirical results further corroborate that MAPS yields more reliable projections in the majority of experimental configurations.
Machine Learning (cs.LG)
arXiv:2610.33209 [cs.LG]
(or arXiv:2610.33209v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.33209 Focus to learn more arXiv-issued DOI via DataCite
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