The paper introduces Error Feedback Calibration (EFC), a test‑time framework that corrects deterministic time‑series forecasts by using revealed errors as causal feedback. EFC builds a Causal Error Diagonal (CED) from overlapping historical forecast errors, applies Cumulative Error Transport (CET) to map this error state to future residuals, and employs Temporal Residual Learning (TRL) to predict remaining residual structure. Experiments across various forecasting backbones and datasets demonstrate that EFC improves performance while limiting over‑correction.
By Haonan Yang, Yulin He, Zhuo Li
arXiv:2505. 15354v3 Announce Type: replace Abstract: Time-series forecasting is a critical task in various business domains, but it remains inherently challenging.
By Hamza Cherkaoui, Malik Tiomoko, Giuseppe Paolo, Zhang Yili, Yu Meng, Zhang Keli, Hafiz Tiomoko Ali
arXiv:2606. 27688v1 Announce Type: cross Abstract: In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed.
By Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
arXiv:2610.07038v1 Announce Type: new
Abstract: Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so pr...
By Weijia Han, Lisha Qu, Zhenda Li, Liying Liang
arXiv:2609.39789v1 Announce Type: cross
Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although r...
By Yeryeong Kwak, Yoo-Min Jung, Jonghun Park
RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.
By Yuchen He, Yueyang Cang, Zhiyuan Ning, Ningyu Wang, Li Shi
arXiv:2608. 10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions.
By Sangjin Jin, Kangmin Kim, Junhyeong Lee, Yongjae Lee
The paper proposes a method for controlling downside risk when adjusting forecasts from frozen models, such as foundation models, by combining a static corrector and an online corrector on the simplex. Using only post‑horizon losses, the approach achieves minimal deterioration (0.15%) and up to 11.5% gains across 28 forecast pairs, and consistently reduces mean MSE in day‑ahead load forecasts for seven European bidding zones. The method’s applicability is bounded by three empirical conditions related to expert speed, stream length, and outcome alignment.
By Minkyoung Kim, Hyunjung Byun, Yohan Lee, Beakcheol Jang
SACQ introduces a structured prediction head for long‑horizon time‑series forecasting that replaces the traditional flatten readout. It uses a two‑stage decoding process: a coarse patch‑grid scaffold is first generated, then each future position is refined via cross‑attention over historical memory, with a learned gate merging the corrections. A batch‑adaptive scaled log‑cosh loss further stabilizes training under long horizons and noisy labels, leading to superior MSE/MAE performance across multiple backbones with minimal overhead.
By Guo Cheng, Zhengzhuo Xu, Chenchen Jing, Jingyi Hou
CARE is a lightweight plug‑in module that can be added to any deterministic time‑series forecaster. It resamples historical data to align with the forecast horizon, learns residual corrections, and applies scale‑aware bounded updates controlled by per‑coordinate sigmoid risk gates. The module is trained with a multi‑objective loss to improve accuracy, track residuals, align risk, and anchor the base model, and it demonstrates accuracy gains on eight benchmarks with minimal extra parameters or latency.
By Guo Cheng, Changlong Lv, Jingyi Hou
arXiv:2610.07834v1 Announce Type: new
Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster...
By Chao He, Jianyu Xu, Xinyi Guo, Ruiqi Liu, Haobin Ding, Ruiqi He, Dongqing Song
EPOC (Endpoint‑Preserving Online Correction) is a method for multi‑horizon time‑series forecasting that compresses residual information using low‑order discrete cosine transform (DCT) coefficients and the final residual value. It applies channel‑wise online ridge regressions to blend a DCT‑based correction with a base forecast, achieving significant reductions in mean squared error (15.40%) and mean absolute error (9.35%) across 96 fixed‑base conditions while keeping auxiliary state small (median 6,352 B). The approach outperforms several existing adapters and offers a lightweight, endpoint‑focused correction strategy.
By Takumi Fujimoto, Hiroaki Nishi