The paper proposes a training‑free continual learning approach for multimodal time‑series forecasting by evolving a textual guidance block that is appended to a frozen large language model’s prompt. On a benchmark combining weekly search indices, daily currencies, and daily commodities with web‑retrieved news, the evolved 900‑word guidance outperforms existing time‑series foundation models and transfers to newer models without loss, while requiring only monetary cost rather than GPU days. The authors also show that compiling the guidance into executable code recovers most of the gain deterministically and that the advantage grows at short horizons, with news text being distilled into policy during learning.
By Laurent Mombaerts, Jonathan Taws, Jacopo Gargano, Dinesh Gaddam, Kevin George, Srijan Tiwari, Shreyas Rajesh
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information.
arXiv:2607. 24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal.
By Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen, Dai Do, Hung Le
arXiv:2610.07834v1 Announce Type: new
Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster...
By Chao He, Jianyu Xu, Xinyi Guo, Ruiqi Liu, Haobin Ding, Ruiqi He, Dongqing Song
Irregular multivariate time series forecasting (IMTSF) requires capturing temporal dynamics and cross-variable dependencies from sparse, asynchronous observations. Existing methods often rely on learn...
arXiv:2609.15087v1 Announce Type: cross
Abstract: Most time series forecasting benchmarks remain numerical-centric and provide limited support for evaluating contextual information that shapes real-w...
By Peng Chen, Zhihao Zhuang, Hongzhou Chen, Junhao Huang, Aiping Yang, Mengsen Wu, Yiding Liu, Xilin Dai, Zewei Dong
arXiv:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.
By Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme
arXiv:2607. 19382v1 Announce Type: cross Abstract: Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary dynamics and limited explainability.
By Quentin Besnard (RFAI), Emmanuel Doumard (BDTLN), Nicolas Labroche (LIFAT, BDTLN), Nicolas Ragot (RFAI), Nicolas Ringuet (BDTLN)
arXiv:2607. 19659v1 Announce Type: new Abstract: Time-series foundation models can forecast across heterogeneous domains without task-specific training, but their forecasts are fixed once produced and cannot directly incorporate task-specific expert feedback.
By Hung Le, Minh Hoang Nguyen, Manh Nguyen, Huu Hiep Nguyen, Dai Do
The paper introduces Context as Covariates (CoCo), a forecasting framework that combines Large Language Models (LLMs) and Time Series Foundation Models (TSFMs). It generates realistic multimodal datasets by annotating real-world time series with LLMs and aligns the two models through staged fine‑tuning: first using Group Relative Policy Optimization to improve the LLM’s covariate generation, then fine‑tuning the TSFM on those covariates. Experiments on synthetic and real benchmarks across finance, economics, weather, traffic, and security show that CoCo can outperform state‑of‑the‑art TSFMs and LLM forecasters by up to 12% MAE.
By Hemanth Ram, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep Chinchali
The paper introduces an agentic forecasting environment built on 2,100+ resolved Polymarket questions, where a language model (Qwen3.5-35B-A3B) learns to gather evidence during rollout via web search, page reading, and financial time series, all filtered to avoid post‑cutoff leaks. Training with single‑epoch GRPO and a Brier‑score reward improves calibration by 30‑40% and reduces search attempts, while the trained policy outperforms four frontier models in evidence‑based forecasting, achieving lower soft‑Brier scores at roughly 5% of the inference cost. The authors release the environment, dataset, and per‑rollout records as a reusable harness for temporal forecasting agents.
By Yusuf Afifi, Artur Kiulian, Anton Polishko, Mykola Khandoga, Hamudi Naanaa, Alina Krasnobrizha
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai