Several forecast-based monitoring methods have been developed for autocorrelated data. One effective method is to use the forecasts based on the exponentially weighted moving average (EWMA). However, during the transition period of dynamic systems, the forecast-based monitoring procedure becomes inadequate due to its use of constant time series model parameters. In this article we present an ad...

In many count data sets, an item may have more than one defect that causes the item to be defective. The Poisson EWMA scheme cannot be used to monitor such defects. The geometric-Poisson exponentially weighted moving average (EWMA) chart has been shown to be an effective scheme to monitor the number of defects over time. In these applications, it is assumed that the process parameters are known...

Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a ne...

Guttman and Tiao [1], and Chang [2] showed that the effect of outliers may cause serious bias in estimating autocorrelations, partial correlations, and autoregressive moving average parameters (cited in Chang et al. [3]). This paper presents a modified weighted symmetric estimator for a Gaussian first-order autoregressive AR(1) model with additive outliers. We apply the recursive median adjustm...

Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, ...

The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are seriallycorrelated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, s...

Statistical process monitoring (SPM) literature recommends the combination of Shewhart and exponentially weighted moving average (EWMA) schemes to improve ability standalone EWMA in detecting small large shifts. The resulting scheme is named combined (or composite) Shewhart-EWMA (CSEWMA) scheme. In this paper, a new single composite (denoted as SCSEWMA) for mean when parameters are known or unk...

We address the problem of online change detection in multivariate datastreams, and we introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a nonparametric change-detection algorithm that can control expected time before false alarm, yielding desired Run Length (ARL$_0$). Controlling alarms is crucial many applications rarely guaranteed by algorithms monitor datastreams without k...