IEEE

Harnessing Forecast Uncertainty in Deep Learning for Time Series Anomaly Detection with Posterior Distribution Scoring

Abstract: Time series anomaly detection tools (TSAD) are widely applicable across industries, such as monitoring time series data of water pipeline pressure, network traffic activities, and hardware ...
IEEE

Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation

Abstract: Multivariate time series forecasting (MTSF) endeavors to predict future observations given historical data, playing a crucial role in time series data management systems. With advancements ...