Abstract
This paper addresses the issue of how we can detect changes of changes, which we call metachanges, in data streams. A metachange refers to a change in patterns of when and how changes occur, referred to as “metachanges along time” and “metachanges along state”, respectively. Metachanges along time mean that the intervals between change points significantly vary, whereas metachanges along state mean that the magnitude of changes varies. It is practically important to detect metachanges because they may be early warning signals of important events. This paper introduces a novel notion of metachange statistics as a measure of the degree of a metachange. The key idea is to integrate metachanges along both time and state in terms of “code length” according to the minimum description length (MDL) principle. We develop an online metachange detection algorithm (MCD) based on the statistics to apply it to a data stream. With synthetic datasets, we demonstrated that MCD detects metachanges earlier and more accurately than existing methods. With real datasets, we demonstrated that MCD can lead to the discovery of important events that might be overlooked by conventional change detection methods.
Highlights
This indicates the effectiveness of Because metachange detection algorithm (MCD)-T depends on discounting parameter r and the change detection algorithm used, we investigated these effects
We propose the concept of metachanges along time and state in data streams, and we introduce metachange statistics to quantify metachanges from a unified view with minimum description length (MDL)
We empirically demonstrated that the proposed algorithm was highly effective in detecting metachanges along time and state
Summary
We are concerned with detecting changes in data streams. The goal of change detection is to detect the time points at which the nature of the data-generating mechanism significantly changes. Metachanges along time indicate that the interval significantly varies between the change points. Such metachanges were called burstiness [12] and volatility [13] in previous studies. In a machine in a manufacturing factory, a decrease in the interval between change points might be a sign of a serious failure There is another type of metachange: metachanges along state. Metachanges along state occur at t6 and t8 with respect to σ: the magnitude of the change of standard deviations around t6 (t8 ) is greater than those around t5 (t7 ). The purpose of this paper is to propose a framework and an approach to detect metachanges along time and state from a unified view with the minimum description length (MDL) [20]. Description and coding with MDL are suitable for quantifying changes, and they enable us to integrate the code lengths of time and state
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
