THE WAVELET RADIAL BASIS MODEL FOR FORECASTING TIME SERIES WITH JUMPS

The research entitled¬† “The Wavelet Radial Basis Model for Forecasting Time Series With Jumps” was conducted by Rukun Santoso under the guidance of Prof. Drs. Subanar, Ph.D., Prof. Dr. Dedi Rosadi, M.Sc., and Dr. Suhartono, M.Sc. in 2017.

The following is the abstract of this research.

ABSTRACT

This dissertation research has resulted new computation mathematics model called as Wavelet Radial Basis (WRB) model. This model can be used for nonlinear time series forecasting, especially when clustering effect was occurred. The model construction consist of three stage, i.e. prepossessing through wavelet transformation, filtering the clustering effect through radial basis function, and estimation of model parameters. If the result of radial basis filtering support the linear regression assumption, then ordinary least squared method can be used for parameters estimation. In other case, an empirical solution becomes an alternative solution. One of the recommended solution can be reached by neural network (NN) method. read more