arXiv:2605.24381v1 Announce Type: new 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 training much like LLMs. In this work, we evaluate foundation models against standard supervised approaches. Rather than focusing solely on aggregate accuracy, we analyze performance across four
The rapid advancement and adoption of large language models (LLMs) are leading researchers to explore their foundational principles in other domains, like time series forecasting.
Foundation models for time series forecasting could drastically reduce overhead for predictive analytics across industries, offering zero-shot capabilities previously unavailable.
Traditional supervised learning methods for time series forecasting, which require extensive feature engineering and domain-specific training, may be supplanted by more generalized foundation models.
- · AI platform providers
- · Businesses needing predictive analytics
- · Data scientists
- · Financial institutions
- · Specialized time series consulting firms
- · Legacy forecasting software vendors
- · Domain-specific feature engineering roles
Foundation models will become a standard tool in various operational forecasting applications.
Reduced barriers to entry for advanced forecasting will democratize predictive analytics across smaller businesses and emerging markets.
The economic value of expert domain knowledge for defining forecasting models will decrease, shifting expertise towards model interpretation and strategic decision-making.
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Read at arXiv cs.LG