PINE LIBRARY
TimeSeriesRecurrencePlot

Library "TimeSeriesRecurrencePlot"
In descriptive statistics and chaos theory, a recurrence plot (RP) is a plot showing, for each moment i i in time, the times at which the state of a dynamical system returns to the previous state at `i`, i.e., when the phase space trajectory visits roughly the same area in the phase space as at time `j`.
```
A recurrence plot (RP) is a graphical representation used in the analysis of time series data and dynamical systems. It visualizes recurring states or events over time by transforming the original time series into a binary matrix, where each element represents whether two consecutive points are above or below a specified threshold. The resulting Recurrence Plot Matrix reveals patterns, structures, and correlations within the data while providing insights into underlying mechanisms of complex systems.
```
~starling7b
___
Reference:
en.wikipedia.org/wiki/Recurrence_plot
github.com/johannfaouzi/pyts/blob/main/pyts/image/recurrence.py
github.com/bmfreis/recurrence_python/blob/master/cross_recurrence.py
github.com/bmfreis/recurrence_cpp/blob/master/CrossRecurrencePlot.cpp
github.com/JuliaDynamics/RecurrenceAnalysis.jl/blob/main/src/matrices/distance_matrix.jl
juliadynamics.github.io/RecurrenceAnalysis.jl/v2.0/rplots/
distance_matrix(series1, series2, max_freq, norm)
Generate distance matrix between two series.
Parameters:
series1 (float): Source series 1.
series2 (float): Source series 2.
max_freq (int): Maximum frequency to inpect or the size of the generated matrix.
norm (string): Norm of the distance metric, default=`euclidean`, options=`euclidean`, `manhattan`, `max`.
Returns: Matrix with distance values.
method normalize_distance(M)
Normalizes a matrix within its Min-Max range.
Namespace types: matrix<float>
Parameters:
M (matrix<float>): Source matrix.
Returns: Normalized matrix.
method threshold(M, threshold)
Updates the matrix with the condition `M(i,j) > threshold ? 1 : 0`.
Namespace types: matrix<float>
Parameters:
M (matrix<float>): Source matrix.
threshold (float)
Returns: Cross matrix.
rolling_window(a, b, sample_size)
An experimental alternative method to plot a recurrence_plot.
Parameters:
a (array<float>): Array with data.
b (array<float>): Array with data.
sample_size (int)
Returns: Recurrence_plot matrix.
In descriptive statistics and chaos theory, a recurrence plot (RP) is a plot showing, for each moment i i in time, the times at which the state of a dynamical system returns to the previous state at `i`, i.e., when the phase space trajectory visits roughly the same area in the phase space as at time `j`.
```
A recurrence plot (RP) is a graphical representation used in the analysis of time series data and dynamical systems. It visualizes recurring states or events over time by transforming the original time series into a binary matrix, where each element represents whether two consecutive points are above or below a specified threshold. The resulting Recurrence Plot Matrix reveals patterns, structures, and correlations within the data while providing insights into underlying mechanisms of complex systems.
```
~starling7b
___
Reference:
en.wikipedia.org/wiki/Recurrence_plot
github.com/johannfaouzi/pyts/blob/main/pyts/image/recurrence.py
github.com/bmfreis/recurrence_python/blob/master/cross_recurrence.py
github.com/bmfreis/recurrence_cpp/blob/master/CrossRecurrencePlot.cpp
github.com/JuliaDynamics/RecurrenceAnalysis.jl/blob/main/src/matrices/distance_matrix.jl
juliadynamics.github.io/RecurrenceAnalysis.jl/v2.0/rplots/
distance_matrix(series1, series2, max_freq, norm)
Generate distance matrix between two series.
Parameters:
series1 (float): Source series 1.
series2 (float): Source series 2.
max_freq (int): Maximum frequency to inpect or the size of the generated matrix.
norm (string): Norm of the distance metric, default=`euclidean`, options=`euclidean`, `manhattan`, `max`.
Returns: Matrix with distance values.
method normalize_distance(M)
Normalizes a matrix within its Min-Max range.
Namespace types: matrix<float>
Parameters:
M (matrix<float>): Source matrix.
Returns: Normalized matrix.
method threshold(M, threshold)
Updates the matrix with the condition `M(i,j) > threshold ? 1 : 0`.
Namespace types: matrix<float>
Parameters:
M (matrix<float>): Source matrix.
threshold (float)
Returns: Cross matrix.
rolling_window(a, b, sample_size)
An experimental alternative method to plot a recurrence_plot.
Parameters:
a (array<float>): Array with data.
b (array<float>): Array with data.
sample_size (int)
Returns: Recurrence_plot matrix.
파인 라이브러리
진정한 트레이딩뷰 정신에 따라 작성자는 이 파인 코드를 오픈 소스 라이브러리로 공개하여 커뮤니티의 다른 파인 프로그래머들이 재사용할 수 있도록 했습니다. 작성자에게 건배! 이 라이브러리는 개인적으로 또는 다른 오픈 소스 출판물에서 사용할 수 있지만, 출판물에서 이 코드를 재사용하는 것은 하우스 룰의 적용을 받습니다.
면책사항
이 정보와 게시물은 TradingView에서 제공하거나 보증하는 금융, 투자, 거래 또는 기타 유형의 조언이나 권고 사항을 의미하거나 구성하지 않습니다. 자세한 내용은 이용 약관을 참고하세요.
파인 라이브러리
진정한 트레이딩뷰 정신에 따라 작성자는 이 파인 코드를 오픈 소스 라이브러리로 공개하여 커뮤니티의 다른 파인 프로그래머들이 재사용할 수 있도록 했습니다. 작성자에게 건배! 이 라이브러리는 개인적으로 또는 다른 오픈 소스 출판물에서 사용할 수 있지만, 출판물에서 이 코드를 재사용하는 것은 하우스 룰의 적용을 받습니다.
면책사항
이 정보와 게시물은 TradingView에서 제공하거나 보증하는 금융, 투자, 거래 또는 기타 유형의 조언이나 권고 사항을 의미하거나 구성하지 않습니다. 자세한 내용은 이용 약관을 참고하세요.