In general, time series is modeled as summation of known information i.e. historical
information components, and unknown information i.e. random component. In wavelet based
model, time series is represented as linear model of wavelet coecients. Wavelet based model
captures the time series feature perfectly when the historical information components dominate
the process. In other hand, it has low enforcement when the random component dominates the
process. This paper proposes an eort to develop the adequateness of wavelet based model,
when the random component dominated the process. By weighted summation, the data is
carried to the new form which has higher dependencies. Consequently, wavelet based model
will work better. Finally, it is hoped that the better prediction of wavelet based model will be
carried to the original prediction in reverting process
