aiqbench analysis
This research introduces DistDF, a novel method for time-series forecasting that addresses the bias inherent in standard mean squared error approaches when dealing with autocorrelated data. By utilizing a joint-distribution Wasserstein discrepancy, the technique provides a tractable and differentiable metric that provably upper-bounds conditional distributional errors, leading to improved performance across diverse models. The work represents a significant theoretical and practical advancement in aligning forecast distributions for more accurate predictive modeling.
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
arXiv:2510.24574v3 Announce Type: replace Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimizing the conditional negative log-likelihood, typically estimated by the mean squared error. However, this estimation proves biased when the label sequence exhibits autocorrelation. In this paper, we propose DistDF, which achieves alignment by minimizing a distributional discrepancy between the conditional distributions of forecast and label sequences. Since such conditional discrepancies are difficult to estimate from finite time-series observations, we introduce a joint-distribution Wasserstein discrepancy for time-series forecasting, which provably upper bounds the conditional discrepancy of interest. The proposed discrepancy is tractable, differentiable, and readily compatible with gradient-based optimization. Extensive experiments show that DistDF improves diverse forecasting models and achieves leading performance. Code is available at https://anonymous.4open.science/r/DistDF-F66B.
Abstract:Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimizing the conditional negative log-likelihood, typically estimated by the mean squared error. However, this estimation proves biased when the label sequence exhibits autocorrelation. In this paper, we propose DistDF, which achieves alignment by minimizing a distributional discrepancy between the conditional distributions of forecast and label sequences. Since such conditional discrepancies are difficult to estimate from finite time-series observations, we introduce a joint-distribution Wasserstein discrepancy for time-series forecasting, which provably upper bounds the conditional discrepancy of interest. The proposed discrepancy is tractable, differentiable, and readily compatible with gradient-based optimization. Extensive experiments show that DistDF improves diverse forecasting models and achieves leading performance. Code is available at this https URL.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2510.24574 [cs.LG]
(or arXiv:2510.24574v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.24574 Focus to learn more arXiv-issued DOI via DataCite
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