stated assumptions.Although guaranteed accurate information about the future is in many cases impossible, prediction can be useful to assist in makingplans about possible developments; Howard H. Stevenson writes that prediction in business "... is at least two things: Important and hard."[1]
Important concepts in regression analysis are the fitted values and residuals. In general, the data doesn’t fall exactly on a line, so the regression equation should include an explicit error term e i : Y i = b 0 + b 1 X i + e i The fitted values, also referred to as the pred...
At the same time, RBF-ANN as a prediction tool has high accuracy in estimating the effect of asphaltene inhibitors on the reduction of asphaltene precipitation in the petroleum industry27. By comparing the Qs index and AUC values obtained by logistic regression, multilayer perceptron artificial ...
An autoregressive integrated moving average model is a form ofregression analysisthat gauges the strength of one dependent variable relative to other changing variables. The model's goal is to predict future securities or financial market moves by examining the differences between values in the series ...
To investigate the independent risk factors of in-hospital mortality, the variables screened by two methods were used in the training set of univariate logistic regression analysis to evaluate the significance of variables. In the univariate logistic regression analysis, the variables significantly related...
A technique is given based on a dynamic multiinterval traffic volume prediction model based on the k-NN nonparametric regression. Also, a system is developed, that is, a kernel smoother for the autoregression function to do short-term traffic-flow prediction, in which functional estimation ...
To improve the prediction accuracy, in A2, the causal convolution mechanism is applied to effectively establish long-range time-series relationships of historical trajectory, resulting in slightly better regression results compared to those of vanilla LSTM. Considering that only the temporal modeling in ...
Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume...
Random coefficient regression and autoregressive models are important in diverse applications such as the classical statistical analysis of random and mixed effects models, the modelling of certain econometric and biological time series, and as a means for image compression. This paper develops nonparametri...
Among them, MSE and MAE are common evaluation metrics for regression tasks in machine learning, and they are often used to measure the difference between the predicted and true values of a model, Therefore, the smaller the MSE and MAE, the smaller the difference between the predicted and true...