From 0c9e245162be81a5f4d274847899c6c8929b96c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Christopher=20Sch=C3=B6lzel?= Date: Mon, 30 Oct 2023 11:23:23 +0100 Subject: [PATCH 1/2] mentions mfhurst in readme --- README.rst | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 26bed95..1d01d21 100644 --- a/README.rst +++ b/README.rst @@ -30,12 +30,19 @@ Currently the following measures are implemented: Positive Lyapunov exponents indicate chaos and unpredictability. Nolds provides the algorithm of Rosenstein et al. (``lyap_r``) to estimate the largest Lyapunov exponent and the algorithm of Eckmann et al. (``lyap_e``) to estimate the whole spectrum of Lyapunov exponents. **Hurst exponent** (``hurst_rs``) - The hurst exponent is a measure of the "long-term memory" of a time series. + The Hurst exponent is a measure of the "long-term memory" of a time series. It can be used to determine whether the time series is more, less, or equally likely to increase if it has increased in previous steps. This property makes the Hurst exponent especially interesting for the analysis of stock data. **detrended fluctuation analysis (DFA)** (``dfa``) DFA measures the Hurst parameter *H*, which is very similar to the Hurst exponent. The main difference is that DFA can be used for non-stationary processes (whose mean and/or variance change over time). +**Generalized Hurst Exponent** (``mfhurst_b``) + The Generalized Hurst Exponent (GHE, H_q or H(q)) can (as the name implies) be seen as a generalization of the Hurst exponent for data series with multifractal properties. + It's origins are however not directly related to Hurst's rescaled range approach, but to the definition of self-affine functions. + + .. warning:: Nolds also supports the GHE implementation by Di Matteo and Aste (2003) as ``mfhurst_dm``. + However, it is not recommended to use this for anything else than reproducing the results of the authors as ``mfhurst_b`` is more stable and mathematically sound. + Example ------- From bfa3b66409924993aa68a6723fa058db21b5c7da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Christopher=20Sch=C3=B6lzel?= Date: Mon, 30 Oct 2023 11:29:18 +0100 Subject: [PATCH 2/2] fixes indentations --- README.rst | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/README.rst b/README.rst index 1d01d21..156c031 100644 --- a/README.rst +++ b/README.rst @@ -23,12 +23,12 @@ Nolds is a small numpy-based library that provides an implementation and a learn Currently the following measures are implemented: **sample entropy** (``sampen``) - Measures the complexity of a time-series, based on approximate entropy + Measures the complexity of a time-series, based on approximate entropy **correlation dimension** (``corr_dim``) - A measure of the *fractal dimension* of a time series which is also related to complexity. + A measure of the *fractal dimension* of a time series which is also related to complexity. **Lyapunov exponent** (``lyap_r``, ``lyap_e``) - Positive Lyapunov exponents indicate chaos and unpredictability. - Nolds provides the algorithm of Rosenstein et al. (``lyap_r``) to estimate the largest Lyapunov exponent and the algorithm of Eckmann et al. (``lyap_e``) to estimate the whole spectrum of Lyapunov exponents. + Positive Lyapunov exponents indicate chaos and unpredictability. + Nolds provides the algorithm of Rosenstein et al. (``lyap_r``) to estimate the largest Lyapunov exponent and the algorithm of Eckmann et al. (``lyap_e``) to estimate the whole spectrum of Lyapunov exponents. **Hurst exponent** (``hurst_rs``) The Hurst exponent is a measure of the "long-term memory" of a time series. It can be used to determine whether the time series is more, less, or equally likely to increase if it has increased in previous steps. @@ -37,12 +37,13 @@ Currently the following measures are implemented: DFA measures the Hurst parameter *H*, which is very similar to the Hurst exponent. The main difference is that DFA can be used for non-stationary processes (whose mean and/or variance change over time). **Generalized Hurst Exponent** (``mfhurst_b``) - The Generalized Hurst Exponent (GHE, H_q or H(q)) can (as the name implies) be seen as a generalization of the Hurst exponent for data series with multifractal properties. - It's origins are however not directly related to Hurst's rescaled range approach, but to the definition of self-affine functions. + The Generalized Hurst Exponent (GHE, H_q or H(q)) can (as the name implies) be seen as a generalization of the Hurst exponent for data series with multifractal properties. + It's origins are however not directly related to Hurst's rescaled range approach, but to the definition of self-affine functions. + + .. warning:: + Nolds also supports the GHE implementation by Di Matteo and Aste (2003) as ``mfhurst_dm``. + However, it is not recommended to use this for anything else than reproducing the results of the authors as ``mfhurst_b`` is more stable and mathematically sound. - .. warning:: Nolds also supports the GHE implementation by Di Matteo and Aste (2003) as ``mfhurst_dm``. - However, it is not recommended to use this for anything else than reproducing the results of the authors as ``mfhurst_b`` is more stable and mathematically sound. - Example -------