The paper introduces Horizon-Resolved eXplanation (HRX), a framework that adds a horizon axis to time‑series forecasting explanations, allowing each forecast step to have its own importance map. HRX operates as a plug‑in for any differentiable forecaster, includes an evaluation protocol that tests the impact of removing top‑ranked inputs, and a rank criterion to decide when horizon resolution is beneficial. Experiments across multiple backbones and datasets demonstrate that incorporating the horizon axis improves explanation quality and that the step‑wise dependence is low‑dimensional, requiring only a few shared maps regardless of forecast length.
By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
arXiv:2606. 16356v1 Announce Type: new Abstract: We study uncertainty quantification for aggregated forecasting tasks such as annual totals and year-over-year growth rates.
By Andro Sabashvili
arXiv:2609.13345v1 Announce Type: cross
Abstract: Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across...
By Donia Besher, Rajdeep Pathak, Madhurima Panja, Tanujit Chakraborty
arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
By Jaehoon Lee, Jun Seo, Seunghan Lee, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn
arXiv:2609.37694v1 Announce Type: cross
Abstract: Diffusion models have recently shown strong potential for probabilistic multivariate time-series forecasting by modeling complex conditional distribu...
By Rui Han, Min Yang, Xu Zhang, Xinghao Yang, Wei Liu, Yongshun Gong
arXiv:2606. 04342v1 Announce Type: cross Abstract: Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a sufficient target.
By Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
The paper critiques the prevalent use of mean squared error (MSE) for evaluating irregular time‑series forecasting, arguing that MSE is biased by timestamp sampling distributions. It introduces the Continuous‑time Squared Error (CSE), an importance‑weighted metric that theoretically offers a tighter asymptotic bound on continuous‑time risk than MSE. A comprehensive benchmark across synthetic, semi‑synthetic, and eight real‑world datasets demonstrates that CSE more accurately recovers continuous‑time risk, revealing limitations of relying solely on MSE.
By Rongwen Li, Haixin Xie, Xiao Wang, Changjian Chen
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
By Jiaze Sun, Kelvin J. L. Koa, Ruiyang Ni, Yize Liu, Haonan Chen, Ke-Wei Huang
The paper investigates when forecasting accuracy can reliably reveal the underlying temporal structure of a time series. It shows that a small forecast margin does not automatically mean structural ambiguity and introduces a stability-based measure that assesses how well different temporal mechanisms can be distinguished given uncertainty in the selection objective. Experiments demonstrate that this stability metric better predicts when forecast-only structural selection succeeds or fails compared to relying solely on forecast margin.
By Qipeng Qian, Yuntao Qian
arXiv:2607. 05450v1 Announce Type: cross Abstract: This paper explores the "Granularity Paradox" in time-series forecasting, wherein finer temporal disaggregation (e.
By Hugo Moreira
Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficiently studied. Existing benchmarks typically use mean squared error (MSE) as the evaluation metric.
arXiv:2608. 14106v1 Announce Type: cross Abstract: When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation.
By Shu Wan, Miles Ma, Hank Zhu, Guangqi Liu, Stephen Wang, Qingsong Wen, Huan Liu