  A physical data (such as astrophysical, geophysical, meteorological etc.) may
appear as an output of an experiment or it may come out as a signal from a
dynamical system or it may contain some sociological, economic or biological
information. Whatever be the source of a time series data some amount of noise
is always expected to be embedded in it. Analysis of such data in presence of
noise may often fail to give accurate information. The method of filtering a
time series data is a tool to clean these errors as possible as we can just to
make the data compatible for further analysis. Here we made an attempt to
develop an adaptive approach of filtering a time series and we have shown
analytically that the present model can fight against the propagation of error
and can maintain the positional importance in the time series very efficiently.
