Real-Time Anomaly Detection Over Massive Data Streams

ISBN-13: 9786200431578 / Angielski / Miękka / 2019 / 104 str.

Zirije Hasani

Opis

Real-time anomaly detection of massive data streams is one of the important research topics nowadays due to the fact that the most of the world data is generated in continuous temporal processes. It addresses various problems in a lot of domains such as health, education, finance, government, etc. In this work, we propose an enhancement of this approach implemented in HW and TDHW forecasting models. The Genetic Algorithm (GA) is applied to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman's MASE measure of deviation and value of the threshold parameter that defines no anomaly confidence interval. We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals to detect the right anomalies and reduce the number of false ones.

O autorze

Hasani, Zirije
Zirije Hasani is born on 21.04.1988 in Gostivar, Macedonia. She is Doctor in Computer Science and actually is University Professor in Kosovo. She is dedicated researcher in the field of anomaly detection in real time Big Data. This book is a result of her six year of research in the area of anomaly detection in real time Big Data.