The control limits of an exponentially weighted moving average (EWMA) control chart should vary with time, approaching asymptotic limits as time increases. However, previous analytic analyses of EWMA charts consider only asymptotic control limits. In this article, the run length properties of EWMAs with time-varying control limits are approximated using nonhomogeneous Markov chains. Comparing t...

A hybrid estimator of the log-spectral density of a stationary time series is proposed. First, a multiple taper estimate is performed, followed by kernel smoothing the log-multiple taper estimate. This procedure reduces the expected mean square error by (π 2 4) 4/5 over simply smoothing the log tapered periodogram. A data adaptive implementation of a variable bandwidth kernel smoother is given.

We discuss the use of orthogonal wavelet transforms in multivariate data analysis methods such as clustering and dimensionality reduction. Wavelet transforms allow us to introduce multiresolution approximation, and multiscale nonparametric regression or smoothing, in a natural and integrated way into the data analysis. Applications illustrate the powerfulness of this new perspective on data ana...

For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predicti...

Smoothing is an important statistical tool in applied analysis of political data. Smoothers can be use to incorporate nonlinearity, deal with duration dependence, and diagnose residual plots. But smoothers invariably require the analyst to make choices about the amount of smoothing to apply and the criteria for this choice amounts to visual examination. Such visual methods can lead to seemingly...

Automatic forecasts of large numbers of univariate time series are often needed in business. It is common to have over one thousand product lines that need forecasting at least monthly. In these circumstances, an automatic forecasting algorithm is an essential tool. Automatic forecasting algorithms must determine an appropriate time series model, estimate the parameters and compute the forecast...

The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, Seasonality (ETS) modeling have been successfully applied resolve problems with estimation. Our research suggests that it would be ideal use ...

Buffer tanks are widely used within the process industry to prevent flow variations from being directly propagated throughout a plant. The capacity of the tank is used to smoothly transfer inlet flow upsets to the outlet. Ideally, the tank thus works as a low pass filter where the available tank capacity limits the achievable flow smoothing. For infrequently occurring upsets, where the system h...