AutoMA: Automated Generation of Multi-level Annotations for Time Series Visualization

Time series data is ubiquitous in people’s daily production and life, and visualizations augmented with annotations can significantly facilitate the understanding of such data and promote downstream tasks. Consequently, numerous annotation tools have been developed to detect and narrate useful patterns within time series visualizations. However, most existing tools can only identify basic factual insights (e.g., increasing or decreasing trends) that are already present in the charts. When users need deeper insights (e.g., predicting future trends) and richer contextual information (e.g., associative patterns between dimensions within and beyond the chart), these tools often fall short. To address this challenge, we present AutoMA, a system that automatically generates multi-level annotations for time series visualizations. We introduce an LLM-based pattern extraction method that supports the identification of seven distinct temporal patterns. Furthermore, we present a multi-level annotation design space that encompasses six specific annotation tasks, aimed at delivering richer contextual information. The generated annotations span a spectrum of information, ranging from directly observable temporal patterns to deeper insights obtained through further computation, and ultimately to advanced inter-dimensional association patterns. Finally, we demonstrate the effectiveness of our approach through experiments and user evaluations. The results indicate that AutoMA significantly enhances users’ ability to comprehend and explore time series data.