Learnijoy
ResearchAI ResearchAI Engineering & DevTools

FM-LLM Adapts LLMs for Superior Time Series Forecasting.

Rentao Gu, Yihang Ding, Junjie Li, Yi Ding, Weijing Sang, Xiaoli Huo, Xin Qin, Yuefeng Ji· August 13, 2026 View original

Key takeaways

  • FM-LLM enhances LLMs for time series forecasting by incorporating frequency dynamics.
  • It uses a Fourier Analysis Network and an asymmetric Mixture-of-Experts decoder.
  • The framework achieves state-of-the-art performance and robust transferability.
  • It offers significant improvements in accuracy and efficiency for forecasting tasks.

Who benefits

FinanceRetailLogisticsEnergyManufacturing

Summary

Researchers propose FM-LLM, a frequency-enhanced mixture-of-experts framework that adapts frozen Large Language Models for time series forecasting without textual prompts. It leverages spectral dynamics and an asymmetric MoE decoder to achieve state-of-the-art performance across multiple benchmarks.

A new framework, FM-LLM (Frequency-Enhanced Mixture-of-Experts for adapting LLMs to Time Series Forecasting), has been introduced to enhance Large Language Models (LLMs) for time series forecasting. This approach avoids the computational overhead of textual prompts by directly integrating frequency-aware information into frozen LLMs. It uses a Fourier Analysis Network (FAN) to inject harmonic representations and an asymmetric Mixture-of-Experts (MoE) decoder to model both global periodic and non-periodic residual dynamics. The framework employs a hybrid time-frequency loss function to optimize both temporal accuracy and spectral consistency, which helps mitigate error accumulation during long-horizon predictions. Evaluated across eleven public benchmarks, FM-LLM achieved state-of-the-art results on a majority of metrics, showing significant improvements over existing LLM-based baselines. It also demonstrated strong transferability in few-shot and zero-shot scenarios.

Why it matters

Professionals in data science and AI engineering can leverage this framework to significantly improve the accuracy and efficiency of time series forecasting models, especially when adapting powerful LLMs to this domain.

How to implement this in your domain

  1. 1Evaluate FM-LLM's performance on internal time series datasets, comparing it against current forecasting methods.
  2. 2Integrate the Fourier Analysis Network (FAN) component to inject spectral dynamics into existing LLM-based forecasting pipelines.
  3. 3Experiment with the asymmetric Mixture-of-Experts (MoE) decoder for specialized modeling of periodic and non-periodic patterns.
  4. 4Consider adopting the time-frequency hybrid loss function to enhance prediction accuracy and consistency over long horizons.

Original post by Rentao Gu, Yihang Ding, Junjie Li, Yi Ding, Weijing Sang, Xiaoli Huo, Xin Qin, Yuefeng Ji

"arXiv:2608.11623v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational ov…"

View on X

Originally posted by Rentao Gu, Yihang Ding, Junjie Li, Yi Ding, Weijing Sang, Xiaoli Huo, Xin Qin, Yuefeng Ji on X · view source

Want to go deeper?

Turn these trends into skills with Learnijoy's hands-on AI & tech courses.

Explore courses

More in AI Research

AI ResearchAI Engineering & DevTools

Task-Vector Interference in Merged LLMs Driven by Orientation, Not Magnitude.

This research reveals that interference in merged language models, often attributed to magnitude, is primarily driven by the orientation of task-vectors. It demonstrates that erasing interference along specific directions causally removes its effects, while magnitude-based interventions are insufficient and inconsistent.

Chencheng ZhuAug 13, 2026
AI Engineering & DevToolsAI Research

New Method Detects Gradual GNSS Spoofing in Autonomous Driving.

This paper proposes a causal high-order liquid evidence framework to detect gradual GNSS spoofing attacks in autonomous driving. By modeling the evolution of GNSS-motion inconsistency with multiple evidence streams and adaptive liquid encoders, the method achieves high F1-scores in detecting subtle spoofing.

Muhammad Ayub Sabir, Junbiao Pang, Fatima AshrafAug 13, 2026
AI ResearchAI Engineering & DevTools

MOON Improves Multitask Learning with OrthoNormalized Gradient Updates.

This paper introduces MOON (Multi-Objective OrthoNormalized Updates), a novel approach for multi-task learning that addresses limitations of Euclidean gradient manipulation in multi-objective optimization. MOON performs gradient manipulation under spectral-nuclear norm geometry, leading to more efficient optimization and improved performance in modern architectures like Transformers.

Shiji Zhou, Kunlin Lyu, Lei Zhang, Ruodong Wang, Yifan SunAug 13, 2026