The paper introduces Forecast‑Aware Residual Refinement (FARR), a method that refines long‑horizon time‑series forecasts by learning to correct predictions using endpoint evidence. FARR generates a valid forecast under normal conditions and a null forecast under a predefined null condition; the difference between these forecasts indicates how auxiliary conditioning affects the prediction. A shared residual refiner then combines this endpoint evidence with historical features to estimate the remaining error, and horizon‑wise gating controls the contribution of the predicted residual to the final output. Experiments on four long‑horizon benchmarks demonstrate that FARR achieves the best average MSE and MAE among compared methods.
By Yiyang Zhang, Yuli Zheng, Bin Li, Meimei Zhang, Qiang Sun
ROAR is a Retrieval Opportunity-Aware Refinement framework designed for zero-shot time series forecasting. It enhances predictions by weighting queries according to base-forecast difficulty and the improvement potential of retrieved historical candidates, then learning a gate to control correction strength. The method jointly calibrates the forecasting module and gate, anchoring the final prediction to an initial refined output, and achieves the lowest average MSE across seven benchmarks compared to other methods.
By Jinjin Chi, Lei Feng, Yiming Wang, Lulu Zhang, Yongcheng Jing, Chenyi Li, Ximing Li
arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.
By Zheng Li, Jerry Cheng, Huanying Gu
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
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
Dual-Context Analog Retrieval for Time Series Forecasting (DuoTS) introduces a two-stage forecasting approach that first generates a base forecast and then refines it patch by patch. Each refinement step fuses a current context, which focuses on recent tokens, with a detail context that incorporates retrieved analogs and their subsequent trajectories. Experiments on real-world datasets demonstrate that DuoTS achieves state‑of‑the‑art performance, and ablation studies confirm the importance of both contexts and the refinement mechanism.
By Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
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
AdaCast introduces a conditional parameter generation framework that tailors time‑series forecasting models to each input by producing low‑rank updates for a frozen pretrained foundation model. Unlike static adaptation methods that apply a single set of updates to all inputs, AdaCast generates input‑specific updates during both training and inference. Experiments on six public benchmarks show that AdaCast consistently outperforms static baselines in in‑domain forecasting and improves zero‑shot generalization to unseen datasets across domains.
By Darahaas Nallagatla, Darryl Cherian Jacob, Pan He
arXiv:2607. 23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training.
By Morad Laglil, Bertrand Pracca, Emilie Devijver, Eric Gaussier
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
SteerCast is a retrieval‑based latent steering technique that enhances decoder‑only time‑series forecasters during inference without updating model parameters. It builds a database from the training set, storing each history window’s representation and a steering vector that captures the difference between the model’s prediction and the ground‑truth continuation in latent space. During testing, SteerCast retrieves nearest neighbors, aggregates their steering vectors, and injects the resulting signal into the forecaster’s hidden states at each autoregressive step, guiding predictions toward trajectories seen in similar training cases. Experiments on diverse multivariate benchmarks and multiple horizons demonstrate consistent accuracy improvements over fine‑tuned backbones and other retrieval‑based baselines, all while requiring no additional training beyond the original fine‑tuning and using only the training set as a retrieval corpus.
By Van Dai Do, Huu Hiep Nguyen, Minh Hoang Nguyen, Hung Le
arXiv:2512.22428v3 Announce Type: replace
Abstract: Post-hoc residual correction can reduce forecasting error, but always-on updates often fail to generalize: when residual signals are weak or noisy,...
By Jianxiang Xie, Yuncheng Hua, Mingyue Cheng, Flora D. Salim, Hao Xue