YEN-CHING CHANG2026-10-162026-10-162017-01-01Pure ID: 15051164Pure UUID: 9368d392-dba7-4597-b03b-9b124197549110.1108/EC-01-2016-0036https://www.scopus.com/pages/publications/85016784799https://scholars.csmu.edu.tw/handle/123456789/22485Purpose: The Hurst exponent has been very important in telling the difference between fractal signals and explaining their signifcance. For estimators of the Hurst exponent, accuracy and effciency are two inevitable considerations. The main purpose of this study is to raise the execution effciency of the existing estimators, especially the fast maximum likelihood estimator (MLE), which has optimal accuracy. Design/methodology/approach: A two-stage procedure combining a quicker method and a more accurate one toestimate the Hurst exponent fromalargetosmall range will bedeveloped. For the best possible accuracy, the data-induction method is currently ideal for the frst-stage estimator and the fast MLE isthe best candidate for the second-stage estimator. Findings: For signals modeled as discrete-time fractional Gaussian noise, the proposed two-stage estimator can save up to 41.18 per cent the computational time of the fast MLE while remaining almost as accurate asthe fast MLE, and even for signals modeledasdiscrete-time fractional Brownian motion, itcan also save about 35.29 per cent except for smaller data sizes. Originality/value: The proposed two-stage estimation procedure is a novel idea. It can be expected that other felds of parameter estimation can apply the concept of the two-stage estimation procedure to raise computational performance while remaining almost as accurate as the more accurate of two estimators.15enjournal article