A common approach to analyzing unevenly spaced time series is to transform the data into equally spaced observations using some form of
interpolation - most often linear - and then to apply existing methods for equally spaced data. However, transforming data in such a way can introduce a number of significant and hard to quantify
biases, especially if the spacing of observations is highly irregular. Ideally, unevenly spaced time series are analyzed in their unaltered form. However, most of the basic theory for
time series analysis was developed at a time when limitations in computing resources favored an analysis of equally spaced data, since in this case efficient
linear algebra routines can be used and many problems have an
explicit solution. As a result, fewer methods currently exist specifically for analyzing unevenly spaced time series data. The
least-squares spectral analysis methods are commonly used for computing a
frequency spectrum from such time series without any data alterations. ==Software==