Augmentation in AI and energy systems refers to enhancing human capabilities or system performance using advanced computational tools, data, and algorithms. At NECLA, augmentation often involves the use of AI-driven modeling, digital twins, and decision support systems that help engineers optimize infrastructure performance, forecast power demand, or detect anomalies. Instead of replacing human operators, augmentation enables more informed and efficient decision-making by integrating real-time sensor data, simulations, and machine learning outputs into the control workflow, especially in complex environments such as smart grids or cyber-physical systems.

Posts

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents

/in /by

Time series data is essential in various applications, including climate modeling, healthcare monitoring, and financial analytics. Understanding the contextual information associated with real-world time series data is often essential for accurate and reliable event predictions. In this paper, we introduce TimeCAP, a time-series processing framework that creatively employs Large Language Models (LLMs) as contextualizers of time series data, extending their typical usage as predictors. TimeCAP incorporates two independent LLM agents: one generates a textual summary capturing the context of the time series, while the other uses this enriched summary to make more informed predictions. In addition, TimeCAP employs a multi-modal encoder that synergizes with the LLM agents, enhancing predictive performance through mutual augmentation of inputs with in-context examples. Experimental results on real-world datasets demonstrate that TimeCAP outperforms state-of-the-art methods for time series event prediction, including those utilizing LLMs as predictors, achieving an average improvement of 28.75% in F1 score.

Read more