Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting

In this paper, we propose a novel approach, called Regularized Least Squares Fuzzy Support Vector Regression, to handle time series forecasting. Two key problems in time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information. The approach requires only a single matrix inversion, and for the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples.