A distributed real-time data prediction framework for large-scale time-series data using stream processing

作     者：Kehe Wu Yayun Zhu Quan Li Ziwei Wu 

作者机构：School of Control and Computer EngineeringNorth China Electric Power UniversityBeijingChina 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2017年第10卷第2期

页      面：145-165页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by“the Fundamental Research Funds for the Central Universities(2015XS72).” 

主　　题：Prediction Real-time Autoregressive integrated moving average Storm Stream processing Time series 

摘      要：Purpose-The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources,e.g.,sensor networks,securities exchange,electric power secondary system,***,the proposed framework should handle several difficult requirements including the management of gigantic data sources,the need for a fast self-adaptive algorithm,the relatively accurate prediction of multiple time series,and the real-time ***/methodology/approach-First,the autoregressive integrated moving average-based prediction algorithm is ***,the processing framework is designed,which includes a time-series data storage model based on the HBase,and a real-time distributed prediction platform based on ***,the work principle of this platform is ***,a proof-of-concept testbed is illustrated to verify the proposed ***-Several tests based on Power Grid monitoring data are provided for the proposed *** experimental results indicate that prediction data are basically consistent with actual data,processing efficiency is relatively high,and resources consumption is ***/value-This paper provides a distributed real-time data prediction framework for large-scale time-series data,which can exactly achieve the requirement of the effective management,prediction efficiency,accuracy,and high concurrency for massive data sources.