Time Series Based Data Explorer and Stream Analysis for Anomaly Prediction

Abstract

All over the world, time series-based anomaly prediction plays a vital role in all walks of life such as medical monitoring in hospitals and climate and environment risks. In the present study, a survey on the methods and techniques for time series data mining and proposes is carried, in order to solve a brand-new problem, time series progressive anomaly prediction. In terms of contents, the first part sketches out the methods that have captured most of the interest of researchers, which include an overview of abnormal prediction problems, a summary of main characteristics of anomaly prediction, and an introduction of anomaly prediction methodology in literature. The second part focuses on the future research trends on the phase/staged abnormal prediction of time series, where a novel time series compression method and a corresponding similarity measure will be designed, which can be explored subsequently. Finally, the related challenges to take this trend are mentioned. It is hoped that this paper can provide a profound understanding of anomaly prediction for the time series-based data mining research field.