Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon
This addresses the challenge of timely anomaly prediction for domains relying on time series data, but it appears incremental as it builds on existing methods with a new dataset.
The paper tackles the problem of predicting anomalies in time series data by incorporating temporal dynamics like delay and horizon, and it demonstrates efficacy with a new benchmark dataset.
Anomaly detection in time series data is a critical challenge across various domains. Traditional methods typically focus on identifying anomalies in immediate subsequent steps, often underestimating the significance of temporal dynamics such as delay time and horizons of anomalies, which generally require extensive post-analysis. This paper introduces a novel approach for time series anomaly prediction, incorporating temporal information directly into the prediction results. We propose a new dataset specifically designed to evaluate this approach and conduct comprehensive experiments using several state-of-the-art methods. Our results demonstrate the efficacy of our approach in providing timely and accurate anomaly predictions, setting a new benchmark for future research in this field.