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Power law spectrums are frequently used to model complex natural processes. Estimation of power law behavior can be severely hampered by temporal gaps in measured data, which can occur frequently for data sets spanning many years. The author presents an algorithm for iterative gap filling through the use of an autoregressive model. This technique is validated with a simulation study, and its benefits are demonstrated through application to measured data sets.