We are given y t, an observed number of presentations, and using a moving window of data up to time t − 1 we estimate (forecast) the mean μ t = E y t ) and the variance σ t 2 = Var (y t ).
Additionally, we propose a set of features extracted from file system specific BSM audit records, as well as an IDS that identifies attacks based on a decision engine that employs one-class classification using a moving window on incoming data.
In order to quantify this phenomenon, we re-plotted the temporal intensity profiles using a moving average of 3 data points to highlight the numerous intensity peaks.
To better understand how p130Cas impacts FA dynamics, temporal fluorescence intensity profiles of the individual FAs evaluated in Fig. 5 were generated using a moving average of five data points to facilitate curve fitting.
In order to show the frequency distribution of QTL of different sizes, we calculated a probability density function (PDF) for both data sets, for both untransformed data and transformed data, by using a moving average method.
However, because the low frequency components of the signal may not be significant, as an alternative to EMD we also detrended the data using a moving average.
Additional processing was applied to the 3D contrast data sets using a moving average filter of ~5 μm in the axial direction and a median filter of ~8 μm radius in the transverse directions.
In these circumstances it was felt that estimation of under reporting using a moving average to smooth yearly birth data would be more than adequate (see below for a theoretical justification).
Next, the data are smoothed using a moving average filter to remove sources of idiosyncratic variation that are unrelated to the cyclical behavior of the variable (the smoothing step is omitted when using quarterly data due to the coarse frequency of the data).
The impact of various tuning and configuration options on forwarding performance is evaluated, focussing on the mechanism used for moving data to and from virtual machines, the algorithm used for scheduling the virtual router tasks, the number of used CPU cores, and the router tasks affinities.
