Foundation Models for Time Series Forecasting

Abstract

Foundation models have emerged as a powerful class of machine learning models, pre-trained on vast amounts of data to capture broad patterns and features. This paper explores the application of foundation models to time series forecasting, a critical task in various domains such as finance, healthcare, and energy management. By leveraging large-scale pre-trained models, time series forecasting can benefit from improved generalization, faster adaptation to new tasks, and reduced need for extensive labeled data. We review the key principles behind foundation models, including their architecture, training processes, and transfer learning capabilities, and discuss how they can be applied to time series prediction tasks. Through empirical studies, we demonstrate the effectiveness of foundation models in comparison to traditional time series forecasting methods, highlighting their potential to handle diverse and complex forecasting problems. Finally, we explore future directions for integrating foundation models with domain-specific knowledge and real-time data for more accurate and robust time series forecasting.