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.
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.
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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.
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.
arXiv AI
Aug 7
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.
Hugging Face Trending Papers
Aug 6
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited. Since RAG has proven effective in enhancing the capabilities of large language models by incorporating relevant external information, retrieving similar time series sequences as references might also improve accuracy in time series forecasting tasks.
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.
arXiv AI
Aug 10
KReF: Training-Free Retrieval for Long-Term Time-Series Forecasting and Predictive Uncertainty
arXiv:2608. 06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models.
arXiv AI
Aug 3
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.
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.
arXiv Machine Learning
Jun 16
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
arXiv:2603. 14709v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance when fusing retrieved samples with the query.
arXiv Machine Learning
Sep 22
Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs
The paper introduces CASP-LLM, a coverage‑aware semantic prompting framework that mitigates bias toward dominant temporal patterns in prompt‑based time series forecasting. Unlike traditional similarity‑based retrieval that selects top‑K candidates by cosine similarity, CASP‑LLM uses usage‑tracking and a saturating‑gate regularizer to diversify prompt selection without adding learnable parameters. Experiments on six long‑term and the M4 short‑term benchmarks show that CASP‑LLM matches or outperforms similarity‑based LLM forecasters in most settings, with failures traced to cross‑batch usage rather than within‑retrieval redundancy.
arXiv Machine Learning
Aug 19
Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
The paper introduces the Continuous Evolution Pool (CEP), a replay‑free framework for online time series forecasting that tackles recurring concept drift. CEP maintains a dynamic pool of specialized forecasters, using lightweight statistical genes to identify concepts, spawn new models when distribution shifts occur, and prune obsolete ones under memory limits. Experiments on real‑world datasets show CEP reduces forecasting error by up to 24% compared to state‑of‑the‑art baselines, especially in scenarios with pronounced recurring drift.