Variance Analysis of Unevenly Spaced Time Series Data

Christine Hackman, Thomas E. Parker

Abstract:	We have investigated the effect of uneven data spacing on the computation of ox(r). Evenly spaced simulated data sets were generated for noise processes ranging from white PM to random walk EM. ox(r) was then calculated for each noise type. Data were subsequently removed from each simulated data set using typical TWSTFT data patterns to create two unevenly spaced sets with average intervals of 2.8 and 3.6 days. ox(r) was then calculated for each sparse data set using two different approaches. First, the missing data points were replaced by linear interpolation and ox(r) calculated from this now full data set. The second approach ignored the fact that the data we unevenly spaced and calculated ox(r) as if the data were equally spaced with average spacing of 2.8 or 3.6 days. Both approaches have advantages and disadvantages, and techniques are presented for correcting errors caused by uneven data spacing in typical TWSTFT data sets.
Published in:	Proceedings of the 27th Annual Precise Time and Time Interval Systems and Applications Meeting November 29 - 1, 1995 The Doubletree Hotel at Horton Plaza San Diego, California
Pages:	323 - 334
Cite this article:	Hackman, Christine, Parker, Thomas E., "Variance Analysis of Unevenly Spaced Time Series Data," Proceedings of the 27th Annual Precise Time and Time Interval Systems and Applications Meeting, San Diego, California, November 1995, pp. 323-334.
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