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Kernel machines, including support vector machines, regularized networks and Gaussian process etc, have been widely used in forecasting. However, standard algorithms are often time consuming. To this end, we propose a new method for imposing the sparsity of kernel regression ma- chines. Different to previous methods, it incrementally finds a set of basis functions that minimizes the primal cost func- tions directly. The main advantage of out method lies in its ability to form very good approximations for kernel re- gression machines with a clear control on the computation complexity as well as the training time. Experiments on two real time series and benchmark Sunspot assess the feasibil- ity of our method.