Sentence examples for regression forecasting from inspiring English sources

Each sensor locally adjusts its sensing frequency based on the linear regression forecasting model.

Moreover, Vapnik expanded SVM to regression forecasting by adding ε-insensitive loss function, and built the SVR theory [32, 33].

Recently, a novel machine learning technique, called support vector machine (SVM), has drawn much attention in the fields of pattern classification and regression forecasting.

The LMFF model was able to improve the average of root mean square error (RMSEave) and average of mean absolute percentage error (MAPEave) values of the multiple linear regression forecasts by about 18% and 21%, respectively.

Poisson regression forecast that the incidence of THRs will increase exponentially over the coming years, with a predicted incidence of 784 total hip replacements per 10 Swedish residents in 2030 and 1,133 in 2040.

In this approach, the stock indices decision model is designed to reflect fluctuations in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) series, which is selected as an optimal input variable in support vector regression load forecasting model at an appropriate timing.

A frequently used type of ANN for regression and forecasting is the two-layer feed-forward perceptron [ 22], also called single hidden layer feed-forward neural network.

An integrated method is used with a traditional four-step-based demand forecast by OKI, and a development of station-based Light Rail Ridership Regression demand forecast by Pelz.

Before applying the multiple linear regression to forecast the crop yield, it's necessary to know the significant attributes from the database.

(5) Suppose the value of b can be inferred empirically, and an optimal segment selected from the time sequence generated through formula (5), then the values for both a and c can be determined using linear regression, whereby forecast production Q t f can be obtained as Q_{t}^{f} = (Q_{1}^{f},Q_{2}^{f}, ldots,Q_{n}^{f} ),quad t = 1, 2, ldots,n.

The aim of our work was to compare a Bayesian network to logistic regression to forecast IgA nephropathy (IgAN) from simple clinical and biological criteria.