Methods › Sequential › Time Series Analysis › DFA (Random Walk)

Detrended fluctuation analysis

DFA (Random Walk)

7 papers tagged archive 2025-07-28

Introduced by C.-K. Peng et al. in Mosaic organization of DNA nucleotides

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time, e.g. power-law decaying autocorrelation function) or 1/f noise.

The obtained exponent is similar to the Hurst exponent, except that DFA may also be applied to signals whose underlying statistics (such as mean and variance) or dynamics are non-stationary (changing with time). It is related to measures based upon spectral techniques such as autocorrelation and Fourier transform.

PaperSource

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Time Series Analysis4
Time Series2
Representation Learning1
Trajectory Modeling1

Usage over time archive 2025-07-28

Papers per year tagged with DFA (Random Walk): 1994 to 2025, peak 3 3 0 1994: 1 paper 1994 1995: 0 papers 1996: 0 papers 1997: 0 papers 1998: 0 papers 1999: 0 papers 1999 2000: 0 papers 2001: 0 papers 2002: 0 papers 2003: 0 papers 2004: 0 papers 2004 2005: 0 papers 2006: 0 papers 2007: 0 papers 2008: 0 papers 2009: 0 papers 2009 2010: 0 papers 2011: 0 papers 2012: 0 papers 2013: 0 papers 2014: 0 papers 2014 2015: 3 papers 2016: 0 papers 2017: 0 papers 2018: 0 papers 2019: 0 papers 2019 2020: 0 papers 2021: 1 paper 2022: 0 papers 2023: 1 paper 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Time Series Analysis

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