A New Model for Multiple Time Series Based on Data Mining

Time series are the important type of data in the world, and time series data mining is one of the most important subfields of data mining. In this paper we propose a model of temporal pattern discovery from multiple time series based on temporal logic. Firstly, multiple time series are transform to multiple event sequences, and then they are synthesized into one event sequence. Secondly, we generate the observation sequence to mining the temporal pattern and the rules based on the interval temporal logic. The algorithm is proposed to mining online frequent episodes and mining change of patterns on mass event sequences. Finally, efficiency of the model and the algorithm is proved through experiments.