Unlocking Time Series Anomaly Detection with LLMs
Exploring LLMs' untapped potential beyond forecasting
This research investigates whether Large Language Models can effectively understand and detect anomalies in time series data in zero-shot and few-shot scenarios.
- Extends LLM applications from forecasting to anomaly detection
- Evaluates LLMs' performance across various time series datasets
- Formulates key hypotheses about LLM capabilities for anomaly detection
- Tests LLMs in practical anomaly detection scenarios
For security professionals, this represents a significant advancement in detecting unusual patterns that might indicate security breaches or system failures, potentially enabling more flexible and adaptable anomaly detection systems without extensive training data.