Applying temporal dependence to detect changes in streaming data

Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil

doi:10.1007/s10489-018-1254-7

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…At the time of writing there exists very little work in the context of handling temporal dependence during stream mining and concept drift detection. The emergence of temporal dependence in streaming data has started to spawn new research, such as using temporal dependence in streaming data to assist in change detection using a Candidate Change Point model [30].…”

Section: Discussionmentioning

confidence: 99%

Data stream mining: methods and challenges for handling concept drift

2019

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Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of-the art algorithmic solutions. Examples of such problems include the unbound size, varying speed and unknown data characteristics of arriving instances from a data stream. The aim of this research is to portray key challenges faced by algorithmic solutions for stream mining, particularly focusing on the prevalent issue of concept drift. A comprehensive discussion of concept drift and its inherent data challenges in the context of stream mining is presented, as is a critical, in-depth review of relevant literature. Current issues with the evaluative procedure for concept drift detectors is also explored, highlighting problems such as a lack of established base datasets and the impact of temporal dependence on concept drift detection. By exposing gaps in the current literature, this study suggests recommendations for future research which should aid in the progression of stream mining and concept drift detection algorithms.

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Section: Discussionmentioning

confidence: 99%

Data stream mining: methods and challenges for handling concept drift

2019

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“…The effects of time upon streams are thus relatively unexplored in this scenario. At the time of this writing, there are very few investigations in assessing temporal dependence in SML literature, as Change Point Detection [4,15,36,42,50]. Finally, to ensure data independence, dynamical system tools can reconstruct the input space to represent all dependencies in terms of a new set of dimensions [11].…”

Section: Related Workmentioning

confidence: 99%

“…6 shows a proposal for a new generation of change detectors that, in parallel to the concept drift detection, discover (i) if there is temporal dependence, (ii) how long this dependence is, and (iii) in case of temporal dependence changes when one stops, and another starts. Determining the number of lags in which a temporal dependence exists is crucial to adapt the model as in Change Point Detection methods [15]. An idea to explore is how to apply the Granger causality test [20] or the (Partial) AutoCorrelation functions incrementally -for example, using a window containing identically distributed and dependant data to train a model.…”

Section: A Unifying Modelmentioning

confidence: 99%

Towards time-evolving analytics: Online learning for time-dependent evolving data streams

Ziffer

Bernardo

Valle

et al. 2023

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Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

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“…At the time of writing there exists little published work which addresses coping with temporal dependence in a data streaming scenario. A recent study by Duong et al (2018) harnesses temporal dependence in streaming data to aid in change detection by way of a Candidate Change Point model. While this method makes use of the existence of temporal dependence in data streams, it does not aid in solving the issues presented by resetting base classifiers while temporal dependence is present.…”

Section: Temporal Dependencementioning

confidence: 99%

Burst Detection-Based Selective Classifier Resetting

Wares¹,

Isaacs²,

Elyan³

2021

J. Info. Know. Mgmt.

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Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-Based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.

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Applying temporal dependence to detect changes in streaming data

Cited by 5 publications

References 42 publications

Data stream mining: methods and challenges for handling concept drift

Data stream mining: methods and challenges for handling concept drift

Towards time-evolving analytics: Online learning for time-dependent evolving data streams

Burst Detection-Based Selective Classifier Resetting

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