@inproceedings{39b7150c30864d0d830681bb8fe17dec,
title = "Short-term performance metrics forecasting for virtual machine to support anomaly detection using hybrid ARIMA-WNN Model",
abstract = "Anomaly detection is a significant functionality in most cloud monitoring applications. Time-series forecasting model could be easily used for predicting the values of the performance metrics which could be used for representing the performance status of the cloud environment. The proposed hybrid model combines both Autoregressive Integrated Moving Average (ARIMA) and Wavelet Neural Network (WNN) models. Firstly, ARIMA model is employed to firstly predict the linear component and then WNN model is used for the nonlinear residual component prediction. Finally, the results of the two parts are combined into the final prediction value of the performance metric. Finally the experimental results show that the hybrid model could produce more accurate short-term prediction than other models.",
keywords = "ARIMA model, Anomaly Detection, Hybrid Model, Performance Forecasting, Wavelet Neural Network",
author = "Juan Qiu and Qingfeng Du and Wei Wang and Kanglin Yin and Liang Chen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE; 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 ; Conference date: 15-07-2019 Through 19-07-2019",
year = "2019",
month = jul,
doi = "10.1109/COMPSAC.2019.10228",
language = "英语",
series = "Proceedings - International Computer Software and Applications Conference",
publisher = "IEEE Computer Society",
pages = "330--335",
editor = "Vladimir Getov and Jean-Luc Gaudiot and Nariyoshi Yamai and Stelvio Cimato and Morris Chang and Yuuichi Teranishi and Ji-Jiang Yang and Leong, \{Hong Va\} and Hossian Shahriar and Michiharu Takemoto and Dave Towey and Hiroki Takakura and Atilla Elci and Susumu Takeuchi and Satish Puri",
booktitle = "Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019",
address = "美国",
}