Statistical analysis such as correlation matrix and factor analysis was performed using the IBM SPSS Statistics.
Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression models.
A numbers of techniques are available for descriptors selection such as correlation based, genetic algorithm based, random forest based etc.
Early signal processing techniques were mainly using time-domain operations such as correlation, convolution, inner product, and signal averaging.
Finally, the accuracy of the developed constitutive equations has been evaluated using standard statistical parameters such as correlation coefficient and average absolute relative error.
Results of both models are compared in terms of statistical measures, such as correlation coefficient, root mean square error and scatter index.
The capability and sensitivity analysis of two models were evaluated by some of statistical parameters such as correlation coefficient and mean square error.
To evaluate the correlation output, metrics such as correlation peak height, peak-to-correlation energy, and peak-to-side lobe ratio are calculated using the simulated results.
A comprehensive suite of experiments involves several types of associative memories such as correlation associative memories, fuzzy associative memories and morphological associative memories.
A comparative study on the application of the aforementioned models is developed based on statistical tools such as correlation coefficient and mean square error.
Proximity metrics, such as correlation coefficient and spatial dispersion, are often used to identify the spatio temporal dynamics in a given framework.
