arXiv:2606. 20010v1 Announce Type: new Abstract: Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2608. 08675v1 Announce Type: new Abstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality.
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.
arXiv:2508. 02753v5 Announce Type: replace-cross Abstract: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales.
arXiv:2603. 24226v4 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have inspired a surge of scaling research in industrial search, advertising, and recommendation systems.
arXiv:2608. 14652v1 Announce Type: cross Abstract: The development of 0.
arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.