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

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.