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.
Large language models use auxiliary conditioning to incorporate additional information into time-series forecasts. However, auxiliary conditioning does not consistently improve accuracy and can increa...
arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.
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.
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...
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,...
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.
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.
arXiv:2610.03494v1 Announce Type: new Abstract: Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidenc...
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...
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.
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.
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.