arXiv AI June 16, 2026 By Alper Y{\i}ld{\i}r{\i}m

HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting

arXiv:2606. 17028v1 Announce Type: cross Abstract: Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic evidence suggests that standard forecasting benchmarks may not require the dense superposed representations that make transformers powerful in other domains.

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

Sep 22

One Patch, Three Roles: What Is Actually Coupled in Autoregressive Time-Series Forecasting?

arXiv:2609.23686v1 Announce Type: new Abstract: Patch-based autoregressive time-series forecasting often ties input representation, learned transitions, and recursive execution to one patch length. W...

By Ziang Li, Yue Huang, Guoxu Zhou, Na Han, Jie Wen, Lunke Fei, Xiaozhao Fang

llms efficiency

arXiv Machine Learning

Jun 10

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

arXiv:2606. 10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into phase drift that pointwise metrics fail to detect.

By Waleed Esmail, Stuart Russell, Jana Klinge, Alexander Kappes, Christine Thomas

rag benchmarks

arXiv AI

Jun 18

A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors

arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.

By David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft

llms computer-vision safety

arXiv Machine Learning

Aug 6

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

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

benchmarks

arXiv AI

Aug 12

Rescene: band-limited stochastic forcing turns a frozen neural weather operator into a climate emulator

arXiv:2608. 09971v1 Announce Type: cross Abstract: Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has produced deterministic models whose medium-range skill matches or exceeds that of the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES).

By Minjong Cheon

llms

arXiv Machine Learning

Jun 2

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

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.

By Peng He, Yao Liu, Yanglei Gan, Run Lin, Yuxiang Cai, Qiao Liu

llms benchmarks safety

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Related stories

One Patch, Three Roles: What Is Actually Coupled in Autoregressive Time-Series Forecasting?

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

Rescene: band-limited stochastic forcing turns a frozen neural weather operator into a climate emulator

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