This paper discuss about Feed Forward Neural
Network (FFNN) modelling by using the discrete wavelet
transform (DWT) as pre-processing to the input and target.
Before the training to the FFNN be done, the wavelet
decomposition is performed from the input and target with the
DWT at a level decomposition and result the approximation
coefficient. After training, the reconstruction process as a postprocessing
will returns the output that be resulted from the
FFNN to the term at first. We call the process as an inverse
discrete wavelet transform (IDWT). The next step is do the
predict in-sample and also predict out of sample from FFNN
with Haar discrete wavelet transform at certain level
decomposition. The FFNN training method that be used is
Levenberg-Marquardt with the logistic sigmoid as activation
function and network architectur is determined before. Then the
model is applied to the financial time series data
