Adaptive nonparametric CUSUM scheme for detecting unknown shifts in location

Liu, Liu; Tsung, Fugee; Zhang, Jian

doi:10.1080/00207543.2013.812260

Cited by 43 publications

(22 citation statements)

References 37 publications

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“…Finally, Liu et al proposed an adaptive nonparametric CUSUM scheme to detect constant shifts in the mean. The proposed chart does not require time‐varying control limits while it is relatively less computationally compared with other nonparametric schemes.…”

Section: Adaptive Cusum and Ewma Chartsmentioning

confidence: 99%

“…Kim et al 57 supplemented the ACUSUM chart with new adaptive runs rules to improve its sensitivity in detecting small mean shifts. Finally, Liu et al 58 proposed an adaptive nonparametric CUSUM scheme to detect constant shifts in the mean. The proposed chart does not require time-varying control limits while it is relatively less computationally compared with other nonparametric schemes.…”

Section: Adaptive Cusum Chartsmentioning

confidence: 99%

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Adaptive Control Charts: Recent Developments and Extensions

Psarakis

2015

Quality & Reliability Eng

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Statistical quality control is a very useful tool in the hands of business in order to improve their products quality. The control charts are the most widely used and the most effective tool of the statistical quality control. In this paper, the substantial recent developments in the design of the adaptive control charts, focusing on the univariate control charts, which allow some of their parameters to change during production, are presented.

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Section: Adaptive Cusum and Ewma Chartsmentioning

confidence: 99%

Section: Adaptive Cusum Chartsmentioning

confidence: 99%

Adaptive Control Charts: Recent Developments and Extensions

Psarakis

2015

Quality & Reliability Eng

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“…Detecting change points in time-series data is also one of the key issues in analysing continuous data points. Several approaches have been proposed in this area including CUmulative SUM method(CUSUM) [9], [10], Bayesian method [11] and Kullback-Leibler Importance Estimation Procedure (KLIEP) [12].…”

Section: Introductionmentioning

confidence: 99%

A New Pattern Representation Method for Time-Series Data

Rezvani

Barnaghi

Enshaeifar

2021

IEEE Trans. Knowl. Data Eng.

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The rapid growth of Internet of Things (IoT) and sensing technologies has led to an increasing interest in time-series data analysis. In many domains, detecting patterns of IoT data and interpreting these patterns are challenging issues. There are several methods in time-series analysis that deal with issues such as volume and velocity of IoT data streams. However, analysing the content of the data streams and extracting insights from dynamic IoT data is still a challenging task. In this paper, we propose a pattern representation method which represents time-series frames as vectors by first applying Piecewise Aggregate Approximation (PAA) and then applying Lagrangian Multipliers. This method allows representing continuous data as a series of patterns that can be used and processed by various higher-level methods. We introduce a new change point detection method which uses the constructed patterns in its analysis. We evaluate and compare our representation method with Blocks of Eigenvalues Algorithm (BEATS) and Symbolic Aggregate approXimation (SAX) methods to cluster various datasets. We have evaluated our algorithm using UCR time-series datasets and also a healthcare dataset. The evaluation results show significant improvements in analysing time-series data in our proposed method.

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“…Luo et al 10 and Wu et al 9 further extended the work of Sparks 11 on adaptive CUSUM control charting procedures. Liu et al 12 investigated the nonparametric adaptive CUSUM chart.…”

Section: Introductionmentioning

confidence: 99%

An adaptive EWMA scheme‐based CUSUM accumulation error for efficient monitoring of process location

Zaman

Lee

Riaz

et al. 2017

Quality & Reliability Eng

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The examination of product characteristics using a statistical tool is an important step in a manufacturing environment to ensure product quality. Several methods are employed for maintaining product quality assurance. Quality control charts, which utilize statistical methods, are normally used to detect special causes. Shewhart control charts are popular; their only limitation is that they are effective in handling only large shifts. For handling small shifts, the cumulative sum (CUSUM) and the exponential weighted moving average (EWMA) are more practical. For handling both small and large shifts, adaptive control charts are used. In this study, we proposed a new adaptive EWMA scheme. This scheme is based on CUSUM accumulation error for detection of wide range of shifts in the process location. The CUSUM features in the proposed scheme help with identification of prior shifts. The proposed scheme uses Huber and Tukey bisquare functions for an efficient shift detection. We have used average run length (ARL) as performance indicator for comparison, and our proposed scheme outperformed some of the existing schemes. An example that uses real‐life data is also provided to demonstrate the implementation of the proposed scheme.

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Adaptive nonparametric CUSUM scheme for detecting unknown shifts in location

Cited by 43 publications

References 37 publications

Adaptive Control Charts: Recent Developments and Extensions

Adaptive Control Charts: Recent Developments and Extensions

A New Pattern Representation Method for Time-Series Data

An adaptive EWMA scheme‐based CUSUM accumulation error for efficient monitoring of process location

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