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A framework based on sparse representation model for time series prediction in smart city

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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.

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Acknowledgements

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), the Research Project for Young and Middle-aged Teachers of Fujian Province (JT180045), the Fujian Collaborative Innovation Center for Big Data Application in Governments, the Fujian Engineering Research Center of Big Data Analysis and Processing.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Zhiyong Yu, Xiangping Zheng, Fangwan Huang & Wenzhong Guo

  2. Hangzhou Key Laboratory for IoT Technology & Application, Zhejiang University City College, Hangzhou, 310015, China

    Lin Sun

  3. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhiwen Yu

Authors
  1. Zhiyong Yu
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  2. Xiangping Zheng
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  3. Fangwan Huang
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  4. Wenzhong Guo
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  5. Lin Sun
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Corresponding author

Correspondence to Fangwan Huang.

Additional information

Zhiyong Yu is an associate professor at College of Mathematics and Computer Science, Fuzhou University, China, also affiliated with Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, and Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, China. He received his PhD from Northwestern Polytechnical University, China in 2011. He was a visiting student at Kyoto University, Japan from 2007 to 2009 and a visiting researcher at TELECOM SudParis, France from 2012 to 2013. His current research interests include pervasive computing, mobile social networks, and crowd sensing.

Xiangping Zheng received the BS degree in computer science and technology from Xiamen University of Technology, China in 2015. Currently, he is a postgraduate student in College of Mathematics and Computer Science, Fuzhou University, China. His current research interests include data mining and machine learning.

Fangwan Huang is a senior lecturer at College of Mathematics and Computer Science, Fuzhou University, China. She received the BS and MS degrees in computer science from Fuzhou University, China in 2002 and 2005. Currently, she is a PhD candidate in College of Physics and Information Engineering, Fuzhou University, China. Her research interests include computational intelligence, big data analysis, and so on.

Wenzhong Guo received the BS and MS degrees in computer science and the PhD degree in communication and information system from Fuzhou University, China in2000, 2003, and 2010, respectively. He completed the postdoctoral fellow at Institute of computer Science, National University of Defense and Technology, China in 2013, and senior visiting scholar at Faculty of Engineering, Information and System, University of Tsukuba, Japan in 2013. He is a professor and dean of College of Mathematics and Computer Science, Fuzhou University. He is also a member of ACM and IEEE. His current research interests include VLSI physical design, wireless sensor networks, big data, image processing, and so on.

Lin Sun received the BS degree in communication engineering in 2001 and MS degree in computer science in 2004 from East China University of Science and Technology, China. He received PhD degree in computer science in 2010 from Zhejiang University, China. Now he is the association professor of Zhejiang University City College, China. His research interests include pattern recognition and pervasive computing.

Zhiwen Yu is a professor and the vice-dean of the School of Computer Science, Northwestern Polytechnical University, China. He received the PhD degree in computer science from Northwestern Polytechnical University, China in 2006. He was a Alexander Von Humboldt fellow with Mannheim University, Germany, and a research fellow with Kyoto University, Japan. His research interests include ubiquitous computing and social network analysis. He is a senior member of the IEEE.

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Yu, Z., Zheng, X., Huang, F. et al. A framework based on sparse representation model for time series prediction in smart city. Front. Comput. Sci. 15, 151305 (2021). https://doi.org/10.1007/s11704-019-8395-7

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