Smoothing Before Estimating Uncertainty, Scaling, and Intermittency: Application to Short Heart Rate Signals

2003/01/24 by David R. Bickel, Bickel, David R.
Computer Science · Economics, Econometrics and Finance · Mathematics · Medicine · #Complex Systems and Time Series Analysis #FOS: Mathematics #Heart Rate Variability and Autonomic Control #Probability (math.PR) #Time Series Analysis and Forecasting #math.PR

paper · pdf · doi:10.48550/arxiv.math/0301292

To appear in Fractals

arxiv created 2003/01/24 · openalex publication_date 2003/01/24 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Three aspects of time series are uncertainty (dispersion at a given time scale), scaling (time-scale dependence), and intermittency (inclination to change dynamics). Simple measures of dispersion are the mean absolute deviation and the standard deviation; scaling exponents describe how dispersions change with the time scale. Intermittency has been defined as a difference between two scaling exponents. After taking a moving average, these measures give descriptive information, even for short heart rate records. For this data, dispersion and intermittency perform better than scaling exponents.

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