The paper introduces a regime‑diagnosis framework for industrial time‑series forecasting, highlighting that canonical loss functions embed fixed statistical priors that are violated in real‑world demand regimes such as zero‑inflation, skewness, and high variability. It proposes the Regime‑wise Relative Bias Vector (RBV) as a metric‑agnostic diagnostic that decomposes bias into an intrinsic floor and an excess attributable to training. A large‑scale study across 13 loss objectives and 60,000+ series demonstrates that regime‑aware diagnosis distinguishes optimization‑from‑bias failures and that regime‑aware training can eliminate pooling‑induced bias that mere capacity scaling cannot.
By Pengyu Nie, Chenglang Xu, Yaoshi Chen, Chaogan Ren, Wei Hu, Chao Yang, Jiangong Zhang
arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.
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
The paper demonstrates that the outcome of a forecasting leaderboard is largely determined by the evaluator’s design choices rather than the models themselves. By fixing the data, horizon, and period, the authors varied three key evaluation decisions—unit of analysis, error pooling, and scoring metric—and showed that each can reverse or eliminate the apparent superiority of any forecasting method. The study also evaluates the practical impact of these choices on a deployed system, revealing that the selection rule captures a significant portion of the potential performance gain, and confirms the findings on an external public dataset.
By Md Rezwanul Islam, Wael Mohammed
arXiv:2608. 14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or expert belief.
By Fabricio F Costa
arXiv:2607. 19383v1 Announce Type: cross Abstract: Pretrained generative foundation models cast forecasting as conditional generation from a learned predictive distribution and forecast unseen series zero-shot.
By Ahmed Cherif
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
arXiv:2609.36966v1 Announce Type: cross
Abstract: Covariate effects vary across contexts and shift over time, requiring forecasters to assess how to use them for each forecasting context. As forecast...
By Donguk Kwon, Wooseok Jeong, Dongha Lee
arXiv:2608. 10433v1 Announce Type: new Abstract: Forecast accuracy does not tell us which past inputs produced a prediction.
By Qipeng Qian, Yuntao Qian
The paper introduces VINTAGE-TS, a revision‑aware time‑series foundation model that separates observation time from information‑availability time. It predicts both the next period’s first‑published value and the value available after a fixed delay, maintaining a joint distribution to capture their dependence and uncertainty. The authors provide a detailed evaluation protocol, software tools for validity‑interval reconstruction and delayed‑label filtering, and a synthetic demonstration with a 25‑configuration sensitivity suite to illustrate performance variability and the impact of hindsight contamination.
By Taimoor Ahmad
The paper investigates when auxiliary context can genuinely improve multi‑modal time series forecasting. It identifies two necessary dataset‑level conditions: the target must not be dominated by a last‑value shortcut (low autocorrelation) and the context must provide additional information beyond history (non‑zero conditional mutual information). Experiments on a large mixture‑of‑experts model and several fusion mechanisms show that only when both conditions hold does context routing yield a substantial reduction in mean‑squared error; otherwise its contribution collapses to a capacity floor.
By Ruizhe Zhou, Gaoyuan Du, Xiaoyang Liu, Haoqi Yao, Deepayan Chakrabarti, Jiating Lin, Yixuan Shen
arXiv:2609.08554v1 Announce Type: new
Abstract: In data-driven training, multivariate time-series forecasting is usually optimized with a scalar loss averaged over samples, variables, and horizons. T...
By Jinwoo Park, Hyeongwon Kang, Pilsung Kang