In principal component analysis, the square root of the variance accounted for by a principal component. Singular value decomposition Reconstruction of any matrix by the weighted sum of rank-one matrices consisting of the outer product of the left and right singular vectors (uvT) multiplied by the...
For the selected sampling locations, the root mean square error (RMSE) and the correlation between the predicted and actual location was estimated. In this way, we evaluated the average error and the suitability of the model to select representative sampling locations (Eq. (5)).(5)RMSE=1n∑...
Discriminant validity was confirmed as follows: the square root of the AVE was larger than the corresponding correlation coefficient between factors. With respect to reliability, the internal consistency showed that the Cronbach alpha values of 0.82 for idealized attributes, 0.85 for idealized behaviors,...
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In Section 3 we quote the load series data performing all tests which are required for further processing by our models (stationarity tests, unit-root tests, etc.). Section 4 is the suggested methodology; it encompasses the mathematical formulation of the proposed models (SARIMAX, Exponential ...
The accuracy of a model elevates when the mean predictive error and standard mean converge to 0, the root mean square error is at its minimal, and the standard root mean square error and the average standard error are closely aligned [28,29]. From our analysis, the soil pH aligns best ...
The metrics for evaluating localization performance include the maximum value and root mean square error (RMSE), while computational efficiency is compared using the average processing time. The first comparison method is ORB-SLAM3, one of the most advanced visual SLAM methods. We also compare the...
The best semivariogram models were selected based on strong spatial dependence (SDC), mean error (ME), root-mean-square error (RMSE), mean standardized error (MSE), root-mean-square standardized error (RMSSE), and average standard error (ASE). If the values of ME, MSE, and ASE are clos...
Data from 1 January 2015 to 8 August 2015 accounting for approximately 73% of the data are selected as training set. The rest of the data are regarded as the testing set. 4.2. Performance Criteria of Prediction Accuracy In this paper, root mean square error (RMSE), mean absolute error (...