Knowledge discovery in time series data with contextual event identification

Abstract

Massive temporal data generated in different domains need to be analysed for decision-making by various applications. This time-series data holds significant contextual knowledge in the form of hidden events. The need for automatic identification of such events is apparent. The lack of effective pattern identification techniques for contextual events suggests the need for efficient event identification methods for various applications. The study aims to propose a contextual event identification methodology in temporal data using exploratory data learning. The exploratory learning algorithm identifies appropriate uncertainty limits in an iterative approach to get desired information gain. Audio music streams and standard text datasets are used to test the method and retrieve contextual events. The result shows a 1.8% improvement for text data compared with an LDA approach and 0.04% improvement in the mean reciprocal rate for music data. Contextual event identification is helpful for different decision-making tasks in machine learning. The proposed system is extendable in different domains such as network, financial or medical, where event identification of temporal data is essential.