  Measurements of optical turbulence time series data using unattended
instruments over long time intervals inevitably lead to data drop-outs or
degraded signals. We present a comparison of methods using both Principal
Component Analysis, which is also known as the Karhunen--Loeve decomposition,
and ARIMA that seek to correct for these event-induced and mechanically-induced
signal drop-outs and degradations. We report on the quality of the correction
by examining the Intrinsic Mode Functions generated by Empirical Mode
Decomposition. The data studied are optical turbulence parameter time series
from a commercial long path length optical anemometer/scintillometer, measured
over several hundred metres in outdoor environments.
