An advanced approach for incremental flexible periodic pattern mining on time-series data

Abstract

Periodic pattern mining is a topic for mining periodic event patterns with sufficient confidence. The resulted patterns are often used to predict future events because they have established confidence that their periodicity will be maintained after the last time of the given data. Therefore, intelligent decision-making through periodic patterns has led to popularity in various applications, such as oil price fluctuations prediction, traffic congestion prediction, human behavior analysis, and sensor-based data analysis in the industry. Meanwhile, previous approaches adopting data structures based on the suffix-tree or trie developed the mining performance of this topic, and a concept that flexible periodic patterns consider don’t-care events in their intermediate events increased the flexibility of the results in this field. However, the data structures have limitations in terms of computing performance. Meanwhile, technological development has made collecting and accumulating data faster and faster. To conduct mining on modern databases which keep increasing incrementally, processing data streams in real time should be considered in the mining method. In this paper, we propose a novel flexible periodic pattern mining approach that can operate on incremental time-series databases by utilizing an enhanced data structure. Moreover, various experiments, such as multivariable analysis, sensitivity analysis, scalability analysis, demonstrate that the proposed approach has superior performance over previously proposed state-of-the-art algorithms in terms of runtime and memory usage.