Model-agnostic Retrieval-Augmented Extended Forecasting for time series

• URL: http://arxiv.org/abs/2608.14054v1
• Date: Fri, 14 Aug 2026 07:59:17 GMT
• Title: Model-agnostic Retrieval-Augmented Extended Forecasting for time series
• Abstract summary: Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities.<n> achieving optimal performance on time series with short or negligible historical data in domain-specific applications typically requires adaptation via either fine-tuning or RAG.<n>This work explores RAG as a more efficient alternative, in particular RAF, and introduces RAEF, a model-agnostic method built upon RAF.
• Score: 8.126818349121537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities. However, achieving optimal performance on time series with short or negligible historical data in domain-specific applications typically requires adaptation via either fine-tuning or RAG. While fine-tuning is effective, it incurs substantial computational costs. This work explores RAG within univariate time series (Retrieval Augmented Generation) as a more efficient alternative, in particular RAF (Retrieval Augmented Forecasting), and introduces RAEF (Retrieval-Augmented Extended Forecasting), a model-agnostic method built upon RAF. RAEF incorporates key refinements to the retrieval and aggregation mechanisms: (1) direct retrieval in input-space rather than embedding-space, reducing inference overhead, and (2) concatenation-based aggregation that preserves temporal structure instead of averaging. Empirical evaluation across multiple benchmark datasets demonstrates that RAEF outperforms RAF in both accuracy and inference overhead. Furthermore, comprehensive comparisons with zero-shot and fine-tuned foundation models show that RAEF achieves competitive or superior performance to fine-tuning while avoiding its computational burden, establishing it as a practical and scalable approach for domain adaptation in time series forecasting.

Related papers

• Stationarity-Aware Retrieval-Augmented Time Series Forecasting [5.059121667323038]
  Time series forecasting relies on historical patterns.<n>Real-world series often exhibit non-stationarity and regime shifts.<n>We propose Stationarity-Aware Retrieval-Augmented Time Series Forecasting.
  arXiv  Detail & Related papers  (2026-06-02T18:47:16Z)
• Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting [18.764728642533615]
  Time series forecasting aims to predict future values for previously unseen series.<n>Recent foundation models achieve strong in-domain performance through large-scale pretraining.<n>We propose FSA, a feature-to-strategy framework for controlled zero-shot univariate forecasting.
  arXiv  Detail & Related papers  (2026-05-31T15:20:14Z)
• Observationally Informed Adaptive Causal Experimental Design [55.998153710215654]
  We propose Active Residual Learning, a new paradigm that leverages the observational model as a foundational prior.<n>This approach shifts the experimental focus from learning target causal quantities from scratch to efficiently estimating the residuals required to correct observational bias.<n> Experiments on synthetic and semi-synthetic benchmarks demonstrate that R-Design significantly outperforms baselines.
  arXiv  Detail & Related papers  (2026-03-04T06:52:37Z)
• Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking [51.56484100374058]
  We evaluate whether a fully automatic, purely feedback-driven ESN can serve as a competitive alternative to widely used statistical forecasting methods.<n>Forecast accuracy is measured using MASE and sMAPE and benchmarked against simple benchmarks like drift and seasonal naive and statistical models.
  arXiv  Detail & Related papers  (2026-02-03T16:01:22Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• RI-Loss: A Learnable Residual-Informed Loss for Time Series Forecasting [13.117430904377905]
  Time series forecasting relies on predicting future values from historical data.<n>MSE has two fundamental weaknesses: its point-wise error fails to capture temporal relationships, and it does not account for inherent noise in the data.<n>We introduce the Residual-Informed Loss (RI-Loss), a novel objective function based on the Hilbert-Schmidt Independence Criterion (HSIC)
  arXiv  Detail & Related papers  (2025-11-13T09:36:00Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion [13.640501360968782]
  FALDA is a novel probabilistic framework for time series forecasting.<n>It incorporates a component-specific architecture, enabling tailored modeling of individual temporal components.<n>Experiments on six real-world benchmarks demonstrate that FALDA consistently outperforms existing probabilistic forecasting approaches.<n>FALDA also achieves superior overall performance compared to state-of-the-art (SOTA) point forecasting approaches, with improvements of up to 9%.
  arXiv  Detail & Related papers  (2025-05-16T14:32:34Z)
• Chain-of-Retrieval Augmented Generation [91.02950964802454]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• Retrieval Augmented Time Series Forecasting [19.71444850959715]
  We argue that the dynamic and event-driven nature of time-series data makes RAG a crucial component of time-series forecasting. We introduce a principled RAG framework for time-series forecasting, called Retrieval Augmented Forecasting (RAF)
  arXiv  Detail & Related papers  (2024-11-12T23:55:11Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Exogenous Data in Forecasting: FARM -- A New Measure for Relevance Evaluation [62.997667081978825]
  We introduce a new approach named FARM - Forward Relevance Aligned Metric. Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series. As a first validation step, we present the application of our FARM approach to synthetic but representative signals.
  arXiv  Detail & Related papers  (2023-04-21T15:22:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.