Wireless sensors in the Internet of things often transmit information only when a state changes to conserve battery life.
However, transforming data in such a way can introduce a number of significant and hard to quantify biases,[1][2][3][4][5] especially if the spacing of observations is highly irregular.
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.
[5][6][7][8][9][10] [11] The least-squares spectral analysis methods are commonly used for computing a frequency spectrum from such time series without any data alterations.