A research team led by Prof. Li Hai from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has ...

Adaptive anomaly detection can also spot hidden anomalies across multiple data dimensions, such as a credit card account that lists transactions in locations hundreds of miles apart at the same time.

Any time-series dataset that needs anomaly detection can utilize EMOD. “I’m most eager to see EMOD applied to monitoring complex real-world systems—especially those where anomalies can have critical ...

Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many experimental research domains. Exathlon is a benchmark for explainable anomaly ...

TITLE: Transformer with Sparse Mixture of Experts for Time-Series Data Prediction in Industrial IoT Systems AUTHORS: Feng Shi, Bolin Li, Weidong Zhang KEYWORDS: Transformer Model, Industrial Internet ...

Multivariate time series anomaly detection is essential for failure management in web application operations, as it directly influences the effectiveness and timeliness of implementing remedial or ...

Time series data, representing observations recorded sequentially over time, permeate various aspects of nature and business, from weather patterns and heartbeats to stock prices and production ...

Time-series data is frequently multivariate, meaning it contains multiple variables or series that can influence each other. Anomaly detection in this context must consider the interplay between ...

Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from ...