Heng Pan’s research while affiliated with Zhongyuan University of Technology and other places

Extracting knowledge from time series provides important tools for many real applications. However, many challenging problems still open due to the stochastic nature of large amount of time series. Considering this scenario, new data mining and machine learning techniques have continuously developed. In this paper, we study time series based on its topological features, observed on a complex network generated from the time series data. Specifically, we present a trend detection algorithm for stochastic time series based on community detection and network metrics. The proposed model presents some advantages over traditional time series analysis, such as adaptive number of classes with measurable strength and better noise absorption. The appealing feature of this work is to pave a new way to represent time series trends by communities of complex networks in topological space instead of physical space (spatial-temporal space or frequency spectral) as traditional techniques do. Experimental results on artificial and real data-sets shows that the proposed method is able to classify the time series into local and global patterns. As a consequence, it improves the predictability on time series.

In machine learning, traditional data classification techniques analyze only physical features of the input data (e.g., distance or distribution) in order to identify the main differences among them. This type of approach is referred to as low level classification. However, the human (animal) brain is able to perform not only low orders of learning, but it is also able to identify patterns according to the semantic meaning of the input data. Data classification that considers both physical attributes and also the pattern formation, is referred to as high level classification. Previous high level classification techniques require a low level technique to work together. Such an approach does not fully highlight the ability of feature extraction embedded in high-level schemes. In this paper, we propose a pure network-based high level classification technique which aims to identify the classes of new instances by detecting and comparing the impact that each one of them has on the topological structure of the network components, which represent each class of the input data set. Eight artificially generated data sets, along with other nine different real classification data sets, were used in order to test this technique, as well as to compare its performance with those obtained by nine traditional and well-known classification models. The results of these tests are very stimulating, indicating that the novel technique proposed in this work may have great potential for further development and application. Moreover, the peculiarity of the concept of pattern based classification provides a new general approach for raw data feature extraction.