---
title: "FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.07870v1"
permalink: "https://feed.astralor.com/items/itm_355b47b6ec2fc335b76c0018"
published: "2026-11-05T11:45:32.000Z"
score: 57
channel: "tech"
tags: ["LLM","RAG","FARR","Forecast-Aware Residual Refinement","Matched Endpoint Evidence","Long-Horizon Time-Series Forecasting","Large","However"]
---

# FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting

## 速览

Large language models use auxiliary conditioning to incorporate additional information into time-series forecasts. However, auxiliary conditioning does not consistently improve accuracy and can increase forecasting erro…

## 关键点

- Large language models use auxiliary conditioning to incorporate additional information into time-series forecasts.
- However, auxiliary conditioning does not consistently improve accuracy and can increase forecasting error when its influence is poorly matched to the observed temporal context.
- We propose Forecast-Aware Residual Refinement (FARR), which learns to correct forecasts using endpoint evidence.

## 全文

Large language models use auxiliary conditioning to incorporate additional information into time-series forecasts. However, auxiliary conditioning does not consistently improve accuracy and can increase forecasting error when its influence is poorly matched to the observed temporal context. We propose Forecast-Aware Residual Refinement (FARR), which learns to correct forecasts using endpoint evidence. FARR obtains a valid forecast under the standard condition and a null forecast under a predefined null condition. Their difference reveals how conditioning changes the prediction. 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. Experiments on four long-horizon forecasting benchmarks show that FARR achieves the best dataset-average MSE and MAE among the compared methods. Code will be released at https://github.com/blossomrichzyy-star/farr-forecast-aware-residual-refinement.

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