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Building the prediction model(s) from the
historical time series has attracted many researchers in last few decades. For
example, the traders of hedge funds and experts in agriculture are demanding
the precise models to make the prediction of the possible trends and cycles.
Even though many statistical or machine learning (ML) models have been
proposed, however, there are no universal solutions available to resolve such
particular problem. In this paper, the powerful forward-backward non-linear
filter and wavelet-based denoising method are introduced to remove the high
level of noise embedded in financial time series. With the filtered time
series, the statistical model known as autoregression is utilized to model the
historical times aeries and make the prediction. The proposed models and
approaches have been evaluated using the sample time series, and the
experimental results have proved that the proposed approaches are able to make
the precise prediction very efficiently and effectively.