Comment on ``Estimating $1/f^\alpha$ scaling exponent from short time series"
P. F. Gora
TL;DR
This paper critiques a recent method for estimating the $1/f^eta$ scaling exponent from short time series, highlighting its potential inaccuracies with undersampled data.
Contribution
It provides a critical analysis of the existing method, emphasizing its limitations and potential for producing incorrect estimates in certain data sampling conditions.
Findings
The method may give inaccurate results with undersampled data.
Short time series pose challenges for reliable $1/f^eta$ exponent estimation.
The critique suggests caution in applying the method to real-world data.
Abstract
A recently proposed method (O. Miramontes, P. Rohani, Physica D 166 (2002) 147) for estimating the scaling exponent in very short time series may give wrong results, especially in case of undersampled data.
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Taxonomy
TopicsHydrological Forecasting Using AI · Time Series Analysis and Forecasting · Anomaly Detection Techniques and Applications