Approximate wavelet-based simulation of long memory processes

Abstract

In this article, we investigate an algorithm for the fast O(N) and approximate simulation of long memory (LM) processes of length N using the discrete wavelet transform. The algorithm generates stationary processes and is based on the notion that we can improve standard wavelet-based simulation schemes by noting that the decorrelation property of wavelet transforms is not perfect for certain LM process. The method involves the simulation of circular autoregressive process of order one. We demonstrate some of the statistical properties of the processes generated, with some focus on four commonly used LM processes. We compare this simulation method with the white noise wavelet simulation scheme of Percival and Walden [Percival, D. and Walden, A., 2000, Wavelet Methods for Time Series Analysis (Cambridge: Cambridge University Press).].

Keywords:

• Approximate simulation
• Long memory processes
• Discrete wavelet transform
• Circular autoregressive processes

Acknowledgements

I would like to thank Dr. Donald B. Percival, the Associate Editor, and an anonymous referee for comments that helped improved this article.