A framework based on sparse representation model for time series prediction in smart city
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A framework based on sparse representation model for time series prediction in smart city Zhiyong YU1 , Xiangping ZHENG1 , Fangwan HUANG Zhiwen YU3 2
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, Wenzhong GUO1, Lin SUN2 ,
1 College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China Hangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou 310015, China 3 School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
c Higher Education Press 2020
Abstract Smart city driven by Big Data and Internet of Things (IoT) 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 representation 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. Keywords sparse representation, smart city, time series prediction, dictionary construction
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Introduction
With the rapid development of Big Data and IoT, the concept of “smart city” has attracted much attention of researchers from academia, industry, and government in recent years. As one of the most cutting-edge issues, the smart city pays attention to perceive, correlate and excavate the key information comprehensively for intelligent management through urban computing, with the target of creating a better life for people and promoting the harmonious and sustainable growth of the city [1]. The applications of smart city have been embodied in various aspects including home automation, medical treatment, traffic management, environmental protection, public services and other fields [2–8]. However, the realization of the smart city still faces many great difficulties. One of them is how to extract and represent Received November 19, 2018; accepted October 14, 2019 E-mail: [email protected]
the sensing knowledge from massive IoT devices in order to realize the proactive event alert of smart city, as these devices record chronologically the changing states with a sampling interval. For example, the smart meter, as an important device of the smart grid, collects power consumption at intervals of 10 or 15 minutes [9]. Obviously, these observations can be viewed as time series, and the prediction of time series is an indispensable part in electric po
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