Weighted least square method and associated time series data problems

Preconditioned Iterative Methods for Weighted Toeplitz Least Square Problems

Weighted least square ensemble networks

Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used way of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose the weighted least square ensemble network. The major difference between this method and the other ensemble methods is that we do not assume that nei...

collocation discrete least square (cdls) method for elasticity problems

a meshless approach, collocation discrete least square (cdls) method, is extended in this paper, for solvingelasticity problems. in the present cdls method, the problem domain is discretized by distributed field nodes. the fieldnodes are used to construct the trial functions. the moving least-squares interpolant is employed to construct the trialfunctions. some collocation points that are indep...

Stochastic conjugate gradient method for least-square seismic inversion problems

With the development of computational power, there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion and least square migration. However, though more advanced than conventional methods, these data fitting methods can be very expensive in terms of computational cost. Recently, vario...

Least Square Support Vector Machines as an Alternative Method in Seasonal Time Series Forecasting

The least square support vector machines (LSSSVM) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of LSSVM model in a seasonal time series forecasting has not been widely investigated. This study aims at developing a LSSVM model to forecast seasonal time series data. To assess the effectiveness of this model, the airl...