# FARR authors propose endpoint evidence to refine long-horizon forecasts [Canonical reading](https://escapemeridian.com/readings/farr-authors-propose-endpoint-evidence-to-refine-long-horizon-c234cf4d) The authors of FARR propose a way to adjust long-horizon time-series forecasts when auxiliary conditioning does not fit the observed temporal context. Auxiliary conditioning can add information to guide a forecast, but the authors say it does not consistently improve accuracy and can increase error when poorly matched. FARR compares a valid forecast under the standard condition with a null forecast under a predefined null condition. The difference, which the authors call endpoint evidence, captures how conditioning changes the prediction. A shared residual refiner combines that evidence with historical features to estimate remaining error, while horizon-wise gating controls the contribution of the predicted residual to the final output. The authors report that experiments on four long-horizon forecasting benchmarks gave FARR the best dataset-average MSE and MAE among the compared methods. The abstract does not identify the benchmarks or compared methods, and the reported results do not establish performance beyond those experiments. The authors say code will be released, but the abstract provides no usable code location. Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Nov 5, 2026 (2026-11-05). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-08T21:32:48.783Z. First observed: 2026-11-06T05:01:06.602Z. Retrieved: 2026-11-08T21:27:07.696Z. - [FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting](https://arxiv.org/abs/2611.07870) ## Limits The evidence is the paper's abstract; it does not identify the four benchmarks or compared methods, or provide experiment details. The reported benchmark results do not establish performance beyond the four benchmarks mentioned. The abstract says code will be released but does not provide a usable code location. ## Open question Which four long-horizon forecasting benchmarks and compared methods underlie the reported dataset-average MSE and MAE results? ## Source-stated The authors propose FARR to correct forecasts using endpoint evidence, because auxiliary conditioning can increase error when poorly matched to temporal context. > auxiliary conditioning does not consistently improve accuracy and can increase forecasting error when its influence is poorly matched to the observed temporal context. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated FARR compares forecasts under standard and predefined null conditions; their difference indicates how conditioning changes the prediction. > Their difference reveals how conditioning changes the prediction. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated A shared residual refiner uses endpoint evidence and historical features to estimate remaining error, while horizon-wise gating controls the residual's contribution. > A shared residual refiner combines this endpoint evidence with historical features to estimate the remaining error of the valid forecast. Horizon-wise gating then controls the contribution of the predicted residual to the final output. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated The authors report the best dataset-average MSE and MAE among compared methods in experiments on four long-horizon forecasting benchmarks. > Experiments on four long-horizon forecasting benchmarks show that FARR achieves the best dataset-average MSE and MAE among the compared methods. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated The authors say code will be released. > Code will be released at this https URL. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source and contributor context Zhang, Yiyang, Zheng, Yuli, Li, Bin, Zhang, Meimei, Sun, Qiang · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Time-series Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values. - [Definition reference](https://developers.google.com/machine-learning/glossary#time-series-analysis) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 8aa5c4a8-6f7f-4b6b-832c-01bfe76970b3. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-08T21:32:48.783Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 251299342ff081a56b16d83b941d0334fb6a5bff0d83e44b5e8233a09bc97bdb. Source text hash: a64336416fec71fea797fb8356d02a42f35565802ac405388ba2b9e5b1ff7be6. ## Revision and source-check history ### Version 1 — 2026-11-08T21:32:48.783Z The authors of FARR propose a way to adjust long-horizon time-series forecasts when auxiliary conditioning does not fit the observed temporal context. Auxiliary conditioning can add information to guide a forecast, but the authors say it does not consistently improve accuracy and can increase error when poorly matched. FARR compares a valid forecast under the standard condition with a null forecast under a predefined null condition. The difference, which the authors call endpoint evidence, captures how conditioning changes the prediction. A shared residual refiner combines that evidence with historical features to estimate remaining error, while horizon-wise gating controls the contribution of the predicted residual to the final output. The authors report that experiments on four long-horizon forecasting benchmarks gave FARR the best dataset-average MSE and MAE among the compared methods. The abstract does not identify the benchmarks or compared methods, and the reported results do not establish performance beyond those experiments. The authors say code will be released, but the abstract provides no usable code location. ## Original source arXiv cs.AI · Article Original source date: Nov 5, 2026 (2026-11-05). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-08T21:32:48.783Z. First observed: 2026-11-06T05:01:06.602Z. Retrieved: 2026-11-08T21:27:07.696Z. - [FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting](https://arxiv.org/abs/2611.07870) ## Limits The evidence is the paper's abstract; it does not identify the four benchmarks or compared methods, or provide experiment details. The reported benchmark results do not establish performance beyond the four benchmarks mentioned. The abstract says code will be released but does not provide a usable code location. ## Open question Which four long-horizon forecasting benchmarks and compared methods underlie the reported dataset-average MSE and MAE results? ## Source-stated The authors propose FARR to correct forecasts using endpoint evidence, because auxiliary conditioning can increase error when poorly matched to temporal context. > auxiliary conditioning does not consistently improve accuracy and can increase forecasting error when its influence is poorly matched to the observed temporal context. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated FARR compares forecasts under standard and predefined null conditions; their difference indicates how conditioning changes the prediction. > Their difference reveals how conditioning changes the prediction. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated A shared residual refiner uses endpoint evidence and historical features to estimate remaining error, while horizon-wise gating controls the residual's contribution. > A shared residual refiner combines this endpoint evidence with historical features to estimate the remaining error of the valid forecast. Horizon-wise gating then controls the contribution of the predicted residual to the final output. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated The authors report the best dataset-average MSE and MAE among compared methods in experiments on four long-horizon forecasting benchmarks. > Experiments on four long-horizon forecasting benchmarks show that FARR achieves the best dataset-average MSE and MAE among the compared methods. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source-stated The authors say code will be released. > Code will be released at this https URL. - [Supporting source](https://arxiv.org/abs/2611.07870) ## Source and contributor context Zhang, Yiyang, Zheng, Yuli, Li, Bin, Zhang, Meimei, Sun, Qiang · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Time-series Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values. - [Definition reference](https://developers.google.com/machine-learning/glossary#time-series-analysis) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 8aa5c4a8-6f7f-4b6b-832c-01bfe76970b3. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-08T21:32:48.783Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 251299342ff081a56b16d83b941d0334fb6a5bff0d83e44b5e8233a09bc97bdb. Source text hash: a64336416fec71fea797fb8356d02a42f35565802ac405388ba2b9e5b1ff7be6. ### uncertain — 2026-11-08T21:32:48.783Z — anthropic/claude-opus-5.5 Publication: 8aa5c4a8-6f7f-4b6b-832c-01bfe76970b3 attribution — pass: The method, the problem framing and the benchmark results are consistently attributed to the paper's authors, for example 'the authors say' and 'the authors report'. The source is the arXiv listing by Yiyang Zhang and co-authors, and the draft does not present their claims as established fact. > Authors:Yiyang Zhang, Yuli Zheng, Bin Li, Meimei Zhang, Qiang Sun summary — uncertain: The mechanism is faithful, but a general reader would struggle. First, the draft drops the abstract's framing that this concerns large language models used for time-series forecasting, which is essential context for the actor and why it matters. Second, central terms are left unexplained: MSE and MAE (error measures), auxiliary conditioning, residual refiner and horizon-wise gating. Smallest repair: state that the setting is LLM-based forecasting, gloss MSE/MAE as average error measures where lower is better, and add tooltips for the remaining terms. The question is substantive and fair. > Large language models use auxiliary conditioning to incorporate additional information into time-series forecasts. date — pass: The originalPublished value of 2026-11-05 matches the v1 submission date shown on the arXiv page. The feed date of 2026-11-06 is correctly not used. > \[Submitted on 5 Nov 2026\] support — pass: Each claim matches the abstract verbatim in substance. The limitations are accurate: the abstract does not name the benchmarks or the compared methods, and the code link appears only as 'this https URL'. The draft does not overstate the benchmark results or imply real-world benefits. > Experiments on four long-horizon forecasting benchmarks show that FARR achieves the best dataset-average MSE and MAE among the compared methods. media — pass: The selected media is the verified direct link to the original arXiv abstract page. This is the most direct way for readers to access the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/c234cf4df29ef74c2bd62688a5f2294780592c9f0f5d4cf2c1da88462ac33e20/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/farr-authors-propose-endpoint-evidence-to-refine-long-horizon-c234cf4d.json)