Sentence examples for moving average analysis from inspiring English sources

A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis.

A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis.

Classification models were developed through decision tree (DT), random forest (RF), artificial neural networks (ANN) and moving average analysis (MAA) using training set comprising 61 derivatives.

Then, the generalized nonstationary Kanai-Tajimi model is applied to simulate the ground acceleration time history using the identified characteristics of the site from preliminary ARMA (Auto Regressive Moving Average) analysis and the site-specific analysis.

Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines.

A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA).

(6) Herd density (numeric): herd density values assigned to the survey points through intersection of the points with a distribution map of herd density generated through moving average neighbourhood analysis [ 31] from livestock census data [ 20]. (7) Herd size (numeric): herd size values assigned to the survey points through intersection of the points with a distribution map of herd sizes [ 21].

We used autoregressive integrated moving average (ARIMA) analysis in SAS V.9.3 27 to model the effects of the introduction of the new packaging on the outcomes of interest, while accounting for background trends, seasonal variation, the effects of television antitobacco advertising, and changes in cigarette costliness.

In this work, three techniques of data processing, including moving average, singular spectrum analysis (SSA), and wavelet multi-resolution analysis (WMRA) in combination with artificial neural networks, were used to improve the estimation of daily flows.

Because the estimate was slightly stronger than those for other moving-averages, we used the four-day moving average in the analysis of specific causes of death and effect modifications.

Autoregressive integrated moving average (ARIMA) intervention analysis of bi-monthly observations of revenues in restaurants and pubs show that the law did not have a statistically significant long-term effect on revenue in restaurants or on restaurant revenue as a share of personal consumption.