A unifying framework for detecting outliers and change points from non-stationary time series data

Yamanishi, Kenji; Takeuchi, Jun’ichi

doi:10.1145/775047.775148

Cited by 212 publications

(119 citation statements)

References 8 publications

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“…Change Finder [2][45][22] is another commonly used method which reduces the problem of change point detection into time series-based outlier detection. This method fits an Auto Regression (AR) model onto the data to represent the statistical behavior of the time series and updates its parameter estimates incrementally so that the effect of past examples is gradually discounted.…”

Section: Reviewmentioning

confidence: 99%

A survey of methods for time series change point detection

2016

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Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

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Section: Reviewmentioning

confidence: 99%

A survey of methods for time series change point detection

2016

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“…Yamanishi and Takeuchi [23] propose a framework for detecting both additive outliers and change-points based on AR (autoregressive) models, which are even more restricted than ARIMA models. Therefore, they do not fit our data at all.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Change-Point Detection Method for Clinical Decision Support System Rule Monitoring

Liu

Wright

Hauskrecht

2017

Artificial Intelligence in Medicine

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A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. The detection should be performed online, that is whenever a new datum arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods.

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“…Yamanishi et al . [23] unified the problems of change detection and outlier detection based on the on-line learning of an autoregressive model. Sharifzadeh et al .…”

Section: Related Workmentioning

confidence: 99%

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Zhang

Zheng

2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called . In the first stage, filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that is effective, efficient, and scalable.

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A unifying framework for detecting outliers and change points from non-stationary time series data

Cited by 212 publications

References 8 publications

A survey of methods for time series change point detection

A survey of methods for time series change point detection

Change-Point Detection Method for Clinical Decision Support System Rule Monitoring

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