Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods

Details Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods

2007-01-22

arxiv.org/abs/physics/0701238v1

Physics

Data Analysis, Statistics and Probability

8 pages, 9 figures, submitted to ICOLAD 2007, City University, London, UK

Scientific paper

10.1088/1742-6596/85/1/012025

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.

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