arXiv AI August 3, 2026 By Yu Sun, Yuan Chang, Xiaohou Shi, Yan Sun

TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion

arXiv:2607. 29459v1 Announce Type: cross Abstract: Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maintenance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 19

Spectral Retrieval-Augmented Time-Series Forecasting

arXiv:2606. 19412v1 Announce Type: new Abstract: Time series forecasting leverages historical patterns to predict future values, but traditional methods face challenges when dealing with complex, non-stationary patterns that are difficult to memorize during training.

By Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le

rag benchmarks

arXiv Machine Learning

Jun 4

Stationarity-Aware Retrieval-Augmented Time Series Forecasting

arXiv:2606. 04135v1 Announce Type: new Abstract: Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully parametric forecasters.

By Shiqiao Zhou, Holger Sch\"oner, Zipeng Wu, Edouard Fouch\'e, IAG Wilson, Shuo Wang

rag

arXiv Machine Learning

Aug 17

Model-agnostic Retrieval-Augmented Extended Forecasting for time series

arXiv:2608. 14054v1 Announce Type: new Abstract: Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities.

By Juan Pablo Villa Serna, Rohan Asthana, Vasileios Belagiannis

rag fine-tuning benchmarks

arXiv Machine Learning

Sep 17

Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision

The paper investigates which historical examples are most useful for time‑series forecasting by defining predictive relevance as the expected future utility conditioned on inference‑time information. It introduces a two‑stage approach: a normalized‑pattern retriever generates a coarse candidate set, and a lightweight MLP reranks these candidates using future‑supervised relevance while keeping inference strictly past‑only. Experiments on six benchmarks show that this reranker improves pattern retrieval and outperforms a matched‑protocol baseline, revealing that historical relevance is structured, domain‑dependent, and not governed by a single universal retrieval rule.

By Yong-Hoon Choi, Kwang-Hyun Park, Youngjin Cho

rag benchmarks

arXiv AI

Jun 16

Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

arXiv:2606. 14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns.

By Shiqiao Zhou, Zipeng Wu, Holger Sch\"oner, Edouard Fouch\'e, IAG Wilson, Shuo Wang

rag multimodal benchmarks

arXiv Machine Learning

Jul 8

Channel-wise Retrieval for Multivariate Time Series Forecasting

arXiv:2604. 05543v2 Announce Type: replace Abstract: Multivariate time series forecasting often struggles to capture long-range dependencies due to fixed lookback windows.

By Junhyeok Kang, Jun Seo, Soyeon Park, Sangjun Han, Seohui Bae, Hyeokjun Choe, Soonyoung Lee

rag benchmarks

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Related stories

Spectral Retrieval-Augmented Time-Series Forecasting

Stationarity-Aware Retrieval-Augmented Time Series Forecasting

Model-agnostic Retrieval-Augmented Extended Forecasting for time series

Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision

Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

Channel-wise Retrieval for Multivariate Time Series Forecasting