The paper introduces a simple, model‑agnostic time‑domain augmentation called Sliding‑Window Reordering with Overlap Averaging. It transforms the joint input‑target sequence into overlapping windows, randomly reorders a fraction of them based on a variance criterion, and reconstructs the sequence by averaging overlaps to generate synthetic samples with controlled variation and minimal temporal distortion. Experiments show strong performance gains across nine long‑term forecasting benchmarks and four short‑term traffic benchmarks, with detailed ablations and diagnostics highlighting the effectiveness of each design choice.
By Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr, Lars Schmidt-Thieme
The paper introduces Physics‑SIMS‑TS, a conditional diffusion model designed for long‑horizon oil and gas production forecasting. It enforces monotone decline through negative guidance, decline‑curve constraints, and isotonic projection during sampling, and incorporates spatial training augmentation and an ensembled stochastic sampler to produce calibrated predictive distributions. Evaluated on over 35,000 wells across three jurisdictions, Physics‑SIMS‑TS achieves the highest accuracy among diffusion forecasters and matches transformer ensembles, with only a 0.5% increase in mean squared error for monotonicity.
By Temesgen Mikael Abraha, Yves Lucet
arXiv:2609.13640v1 Announce Type: new
Abstract: Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the fin...
By Bahaeddine Abdessalem, Shifeng Xie, Zehao Xiao, Youssef Attia El Hili, Ambroise Odonnat, Jianfeng Zhang, Lujia Pan, Keli Zhang, Malik Tiomoko
SPACE is a conformal wrapper that creates ellipsoidal joint prediction regions for multivariate time‑series forecasts by estimating time‑local covariance directly from the current forecast sample cloud. It calibrates the region’s radius using a dynamic backward window‑selection scheme, avoiding reliance on historical residuals. Experiments on diverse datasets show that SPACE improves joint and rolling coverage, achieving better coverage‑efficiency tradeoffs than existing wrappers.
By Baishi Li, Kelvin J. L. Koa, Ke-Wei Huang
arXiv:2610.07080v1 Announce Type: new
Abstract: Attention maps are often interpreted as evidence of what a forecasting model uses when making predictions. In our load-forecasting model, a CLS represe...
By Yuji Akamatsu, Takao Yamanaka
arXiv:2609.23686v1 Announce Type: new
Abstract: Patch-based autoregressive time-series forecasting often ties input representation, learned transitions, and recursive execution to one patch length. W...
By Ziang Li, Yue Huang, Guoxu Zhou, Na Han, Jie Wen, Lunke Fei, Xiaozhao Fang