arXiv Machine Learning June 16, 2026 By Tien Thanh Thach

Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation

Read the original on arXiv Machine Learning →

arXiv:2606. 15701v1 Announce Type: new Abstract: Transformers have shown remarkable success in sequence modeling, yet their direct application to financial time series remains challenging due to noisy signals, short-memory dynamics, and distributional shifts.

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

arXiv AI

Jun 30

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.

By Dishan Sarkar

llms benchmarks safety

arXiv Machine Learning

Aug 4

AROpt: An Optimization Method for Autoregressive Time Series Forecasting

arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.

By Zheng Li, Jerry Cheng, Huanying Gu

llms benchmarks

arXiv Machine Learning

Jul 20

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

arXiv:2607. 15705v1 Announce Type: new Abstract: Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems.

By Matthias Hertel, Sebastian P\"utz, Jonathan Kolar, Benjamin Sch\"afer, Ralf Mikut, Veit Hagenmeyer

llms benchmarks

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 AI

Jul 2

EVOTS: Evolutionary Transformer Search for Time Series Forecasting

arXiv:2607. 00154v1 Announce Type: cross Abstract: Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transformer architectures despite substantial variation across tasks and forecasting settings.

By AbdElRahman ElSaid, Damir Pulatov

llms benchmarks

arXiv Machine Learning

Jun 4

Post-Training Corrections for Improved Time-Series Forecasting

arXiv:2505. 15354v3 Announce Type: replace Abstract: Time-series forecasting is a critical task in various business domains, but it remains inherently challenging.

By Hamza Cherkaoui, Malik Tiomoko, Giuseppe Paolo, Zhang Yili, Yu Meng, Zhang Keli, Hafiz Tiomoko Ali

benchmarks

Related stories

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

AROpt: An Optimization Method for Autoregressive Time Series Forecasting

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

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

EVOTS: Evolutionary Transformer Search for Time Series Forecasting

Post-Training Corrections for Improved Time-Series Forecasting