Exploring Multi-Modal LLMs for Time Series Anomaly Detection

Anomaly detection in time series data is crucial across various domains. Traditional methods often struggle with continuously evolving time series requiring adjustment, whereas large language models (LLMs) and multi-modal LLMs (MLLMs) have emerged as promising zero-shot anomaly detectors by leveraging embedded knowledge. This study expands recent evaluations of MLLMs for zero-shot time series anomaly detection by exploring newer models, additional input representations, varying input sizes, and conducting further analyses. Our findings reveal that while MLLMs are effective for zero-shot detection, they still face limitations, such as effectively integrating both text and vision representations or handling longer input lengths. These challenges unveil diverse opportunities for future improvements.