Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge ...Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61772136,61672159)the Technology Innovation Platform Project of Fujian Province(2014H2005)+1 种基金the Research Project for Young and Middle-aged Teachers of Fujian Province(JT 180045)the Fujian Collaborative Innovation Center for Big Data Application in Governments,the Fujian Engineering Research Center of Big Data Analysis and Processing.
文摘Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.
