2019
DOI: 10.3390/e21121134
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Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle

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… Show more

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Cited by 5 publications
(2 citation statements)
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References 30 publications
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“…For systems operating in a complex, changing environment, it can be critical for agents to manage the trade-off between being robustly stable to unimportant fluctuations, disturbances, and false-positives and responsive and adaptable to important environmental cues, even if they are weak or rare. The trade-off between robustness and adaptability is fundamental to many fields, including control and decision-making (Bizyaeva et al, 2022) and on-line learning (Fukushima et al, 2021); in neural systems, it is called the stability–flexibility dilemma (Liljenström, 2003). Collective intelligence for managing this trade-off can be measured in various ways.…”
Section: Collective Intelligence and Designmentioning
confidence: 99%
“…For systems operating in a complex, changing environment, it can be critical for agents to manage the trade-off between being robustly stable to unimportant fluctuations, disturbances, and false-positives and responsive and adaptable to important environmental cues, even if they are weak or rare. The trade-off between robustness and adaptability is fundamental to many fields, including control and decision-making (Bizyaeva et al, 2022) and on-line learning (Fukushima et al, 2021); in neural systems, it is called the stability–flexibility dilemma (Liljenström, 2003). Collective intelligence for managing this trade-off can be measured in various ways.…”
Section: Collective Intelligence and Designmentioning
confidence: 99%
“…It is a challenging new problem: how can we detect signs of changes when they are gradual or incremental? There are a number of studies on model change sign detection [25]- [29]. However, the change signs studied there have not been related to changes at different levels; therefore, the causes of changes in signs can not be explained.…”
Section: Related Workmentioning
confidence: 99%