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.

大数据和物联网（IoT）驱动的智慧城市已成为未来最有希望的趋势。作为智慧城市的一项重要功能，基于时间序列预测的事件警报面临着如何从物联网设备生成的海量顺序数据中提取和表示感知知识的区分特征的挑战。在本文中，提出了一种基于稀疏表示模型（SRM）的时间序列预测框架，作为应对这一挑战的有效方法。在将超完备字典划分为上下两部分之后，SRM的主要思想是首先基于上部获得时间序列的稀疏表示，然后基于下部实现对未来价值的预测。选择不同的字典会对SRM的性能产生重大影响。本文着重于词典构建策略的研究，并总结了SRM的八个变体。实验结果表明，SRM可以灵活有效地处理不同类型的时间序列预测。