Tracing of Error in a Time Series Data

A physical (e.g. astrophysical, geophysical, meteorological etc.) data may appear as an output of an experiment 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. Although text book data filtering theory is primarily concerned with the presences of random, zero mean errors; but in reality, errors in data are often systematic rather than random. In the present paper we produce different models of systematic error in the time series data. This will certainly help to trace the systematic error present in the data and consequently that can be removed as possible to make the data compatible for further study.