Assessing the Operational Viability of Foundation Models for Time Series Forecasting 事件

Assessing the Operational Viability of Foundation Models for Time Series Forecasting arXiv:2605.24381v1 Announce Type: cross Abstract: Time series forecasting drives operational decisions in areas like finance, transportation, and energy. While supervised learning approaches achieve strong performance, they require domain-specific training, feature engineering, and ongoing maintenance. Large-scale foundation models have recently emerged as a zero-shot alternative, avoiding task-specific trainin