arXiv Machine Learning June 2, 2026 By Peng He, Yao Liu, Yanglei Gan, Run Lin, Yuxiang Cai, Qiao Liu

FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2606. 01306v1 Announce Type: new Abstract: While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, systematically smoothing out high-frequency signals vital for sharp local changes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman

llms benchmarks safety

arXiv Machine Learning

Jun 5

FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting

arXiv:2408. 11336v3 Announce Type: replace Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns.

By Tajamul Ashraf, Janibul Bashir

llms benchmarks safety

arXiv AI

Jun 18

Beyond Similarity: Temporal Operator Attention for Time Series Analysis

arXiv:2605. 11287v2 Announce Type: replace-cross Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers.

By Jevon Twitty, Vinh Pham, Nitiwith Rotchanarak, Viresh Pati, Yubin Kim, Shihao Yang, Jiecheng Lu

llms benchmarks

arXiv Machine Learning

Jun 5

Adaptive Oscillatory-State Alignment for Time Series Forecasting

arXiv:2606. 06010v1 Announce Type: new Abstract: Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure.

By Zhangyao Song, Ziqiong Li, Xiangfei Qiu, Chao Zha, Yinfei Xu, Tao Guo

efficiency benchmarks safety

arXiv Machine Learning

Jun 9

FRWKV+: Periodic-Aware Adaptive Gating for Frequency-Space Linear Time Series Forecasting

arXiv:2605. 15690v2 Announce Type: replace Abstract: Accurate and efficient long-term multivariate time series forecasting requires capturing recurring temporal structure while keeping inference cheap across many variables and horizons.

By Qingyuan Yang, Dongyue Chen, Da Teng, Junhua Xiao, Jiaji Pan, Shizhuo Deng

llms benchmarks

arXiv AI

Aug 13

FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting

arXiv:2608. 11623v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting.

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

llms multimodal benchmarks safety

Related stories

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting

Beyond Similarity: Temporal Operator Attention for Time Series Analysis

Adaptive Oscillatory-State Alignment for Time Series Forecasting

FRWKV+: Periodic-Aware Adaptive Gating for Frequency-Space Linear Time Series Forecasting

FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting