Moving Average (MA) Process Model

2011 ◽  
Keyword(s):  
Process Model ◽  
Moving Average ◽  
Ma Process

Moving Average (MA) Process Model

2008 ◽  
pp. 732-732
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Process Model ◽  
Moving Average ◽  
Ma Process

Pattern Mining for Outbreak Discovery Preparedness

Data Mining ◽  
2013 ◽  
pp. 2057-2068
Author(s):  
Zalizah Awang Long ◽  
Abdul Razak Hamdan ◽  
Azuraliza Abu Bakar ◽  
Mazrura Sahani
Keyword(s):  
False Alarm ◽  
Process Model ◽  
Pattern Mining ◽  
Moving Average ◽  
False Positive Rate ◽  
Outbreak Detection ◽  
Positive Rate ◽  
Knowledge Rules ◽  
Overall Performance ◽  
Public Health Surveillance System

Today, the objective of public health surveillance system is to reduce the impact of outbreaks by enabling appropriate intervention. Commonly used techniques are based on the changes or aberration in health events when compared with normal history to detect an outbreak. The main problem encountered in outbreaks is high rates of false alarm. High false alarm rates can lead to unnecessary interventions, and falsely detected outbreaks will lead to costly investigation. In this chapter, the authors review data mining techniques focusing on frequent and outlier mining to develop generic outbreak detection process model, named as “Frequent-outlier” model. The process model was tested against the real dengue dataset obtained from FSK, UKM, and also tested on the synthetic respiratory dataset obtained from AUTON LAB. The ROC was run to analyze the overall performance of “frequent-outlier” with CUSUM and Moving Average (MA). The results were promising and were evaluated using detection rate, false positive rate, and overall performance. An important outcome of this study is the knowledge rules derived from the notification of the outbreak cases to be used in counter measure assessment for outbreak preparedness.

Pattern Mining for Outbreak Discovery Preparedness

2012 ◽  
pp. 125-137
Author(s):  
Zalizah Awang Long ◽  
Abdul Razak Hamdan ◽  
Azuraliza Abu Bakar ◽  
Mazrura Sahani
Keyword(s):  
False Alarm ◽  
Process Model ◽  
Pattern Mining ◽  
Moving Average ◽  
False Positive Rate ◽  
Outbreak Detection ◽  
Positive Rate ◽  
Knowledge Rules ◽  
Overall Performance ◽  
Public Health Surveillance System

Today, the objective of public health surveillance system is to reduce the impact of outbreaks by enabling appropriate intervention. Commonly used techniques are based on the changes or aberration in health events when compared with normal history to detect an outbreak. The main problem encountered in outbreaks is high rates of false alarm. High false alarm rates can lead to unnecessary interventions, and falsely detected outbreaks will lead to costly investigation. In this chapter, the authors review data mining techniques focusing on frequent and outlier mining to develop generic outbreak detection process model, named as “Frequent-outlier” model. The process model was tested against the real dengue dataset obtained from FSK, UKM, and also tested on the synthetic respiratory dataset obtained from AUTON LAB. The ROC was run to analyze the overall performance of “frequent-outlier” with CUSUM and Moving Average (MA). The results were promising and were evaluated using detection rate, false positive rate, and overall performance. An important outcome of this study is the knowledge rules derived from the notification of the outbreak cases to be used in counter measure assessment for outbreak preparedness.

scholarly journals Best Lag Window for Spectrum Estimation of Law Order MA Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ali Sami Rashid ◽  
Mohammed Jabber Hawas Allami ◽  
Ahmed Kareem Mutasher
Keyword(s):  
Computer Simulation ◽  
Spectral Density ◽  
Density Function ◽  
Moving Average ◽  
Main Tool ◽  
Spectral Density Function ◽  
Spectrum Estimation ◽  
Ma Process ◽  
Special Case

In this article, we investigate spectrum estimation of law order moving average (MA) process. The main tool is the lag window which is one of the important components of the consistent form to estimate spectral density function (SDF). We show, based on a computer simulation, that the Blackman window is the best lag window to estimate the SDF of MA1 and MA2 at the most values of parameters βi and series sizes n, except for a special case when β=−1 and n≥40 in MA1. In addition, the Hanning–Poisson window appears as the best to estimate the SDF of MA2 when β1=β2=−0.5 and n≥40.

A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Kehinde Adekunle Bashiru
Keyword(s):  
Process Model ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Probability Generating Function ◽  
Information Criteria ◽  
Data Series ◽  
Suitable Model ◽  
Birth Process ◽  
Autoregressive Integrated Moving Average

In this study the stochastic process model for estimating the incidence of tuberculosis (TB) infection in Ede kingdom (Ede North and Ede South Local Government Areas) of Osun State was carried out. The probability generating function approach was used to solve the associated birth process model to obtain the estimate of TB incidence. Also time series analysis was carried out using JMulti software to predict future incidence rate of the disease in the study area. Based on Autoregressive Integrated Moving Average (ARIMA) model, the autocorrelation and partial autocorrelation methods and a suitable model to forecast TB infection was obtained.  The goodness of fit was measured using the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Having satisfied all the model assumptions ARIMA (0,1,1) model with standard error, 6.37086 was found to be the best model for the forecast. It was observed that the forecasted series were close to the actual data series

scholarly journals Decoding the radial velocity variations of HD 41248 with ESPRESSO

2020 ◽  
Vol 635 ◽  
pp. A13 ◽  
Author(s):  
J. P. Faria ◽  
V. Adibekyan ◽  
E. M. Amazo-Gómez ◽  
S. C. C. Barros ◽  
J. D. Camacho ◽  
...  
Keyword(s):  
Light Curve ◽  
Radial Velocity ◽  
Gaussian Process ◽  
Process Model ◽  
Moving Average ◽  
Rotation Period ◽  
Magnetic Activity ◽  
Noise Model ◽  
Stellar Rotation ◽  
Gaussian Process Model

Context. Twenty-four years after the discoveries of the first exoplanets, the radial-velocity (RV) method is still one of the most productive techniques to detect and confirm exoplanets. But stellar magnetic activity can induce RV variations large enough to make it difficult to disentangle planet signals from the stellar noise. In this context, HD 41248 is an interesting planet-host candidate, with RV observations plagued by activity-induced signals. Aims. We report on ESPRESSO observations of HD 41248 and analyse them together with previous observations from HARPS with the goal of evaluating the presence of orbiting planets. Methods. Using different noise models within a general Bayesian framework designed for planet detection in RV data, we test the significance of the various signals present in the HD 41248 dataset. We use Gaussian processes as well as a first-order moving average component to try to correct for activity-induced signals. At the same time, we analyse photometry from the TESS mission, searching for transits and rotational modulation in the light curve. Results. The number of significantly detected Keplerian signals depends on the noise model employed, which can range from 0 with the Gaussian process model to 3 with a white noise model. We find that the Gaussian process alone can explain the RV data while allowing for the stellar rotation period and active region evolution timescale to be constrained. The rotation period estimated from the RVs agrees with the value determined from the TESS light curve. Conclusions. Based on the data that is currently available, we conclude that the RV variations of HD 41248 can be explained by stellar activity (using the Gaussian process model) in line with the evidence from activity indicators and the TESS photometry.

scholarly journals FREE INTERACTOR MATRIX METHOD FOR CONTROL PERFORMANCE ASSESSMENT OF MULTI-VARIATE SYSTEMS

2012 ◽  
pp. 255-260
Author(s):  
BEHROOZ KHEIRI SARABI ◽  
D. K. MAGHADE ◽  
G. M. MALWATKAR
Keyword(s):  
Performance Assessment ◽  
Performance Index ◽  
Process Model ◽  
Moving Average ◽  
A Priori ◽  
Autoregressive Moving Average ◽  
Control Loop ◽  
Limited Data ◽  
Healthy Working ◽  
Control Loop Performance

In this paper, an alternative method for the assessment of multi-vitiate control loop performance with consider twocircumstances. First, known time delays between each pair of inputs and outputs, and second, without relying on any a priori knowledge about the process model or timedelays. The performance of the control loop is calculated from data driven autoregressive moving average (ARMA) and prediction error model. It is clear that the limited data in scalar measure used for performance assessment results tends to steady-state as time tends to infinity, but large number of samples gives risen in scalar measures and tends to infinity as time samples tends to infinity and therefore it becomes difficult to calculate the performance index. In this paper, the later problem is solved by considering initial part of scalar measures with steady value for next-to-next time samples to calculate the control-loop performance index which would be utilized to decide healthy working of the control loop. Simulation example is included to show the performance index of multi-variate control loop.

Process \'prǎ|,ses\n: The Action of Moving Forward Progressively from One Point to Another on the Way to Completion

1979 ◽  
Vol 44 (1) ◽  
pp. 3-30 ◽  
Author(s):  
Carol A. Pruning
Keyword(s):  
Process Model ◽  
Communicative Behaviors ◽  
Adult Level ◽  
Remedial Programs ◽  
Stage Process ◽  
The Way

A rationale for the application of a stage process model for the language-disordered child is presented. The major behaviors of the communicative system (pragmatic-semantic-syntactic-phonological) are summarized and organized in stages from pre-linguistic to the adult level. The article provides clinicians with guidelines, based on complexity, for the content and sequencing of communicative behaviors to be used in planning remedial programs.

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