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TimeSeriesRecurrencePlotLibrary "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 github.com github.com github.com github.com juliadynamics.github.io 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   Parameters:      M (matrix) : Source matrix.   Returns: Normalized matrix. method threshold(M, threshold)   Updates the matrix with the condition `M(i,j) > threshold ? 1 : 0`.   Namespace types: matrix   Parameters:      M (matrix) : 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) : Array with data.      b (array) : Array with data.      sample_size (int)   Returns: Recurrence_plot matrix.
Pine Script™ library
by RicardoSantos
5
FunctionPolynomialFitLibrary "FunctionPolynomialFit" Performs Polynomial Regression fit to data. In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. reference: en.wikipedia.org www.bragitoff.com gauss_elimination(A, m, n) Perform Gauss-Elimination and returns the Upper triangular matrix and solution of equations.   Parameters:      A : float matrix, data samples.      m : int, defval=na, number of rows.      n : int, defval=na, number of columns.   Returns: float array with coefficients. polyfit(X, Y, degree) Fits a polynomial of a degree to (x, y) points.   Parameters:      X : float array, data sample x point.      Y : float array, data sample y point.      degree : int, defval=2, degree of the polynomial.   Returns: float array with coefficients. note: p(x) = p * x**deg + ... + p interpolate(coeffs, x) interpolate the y position at the provided x.   Parameters:      coeffs : float array, coefficients of the polynomial.      x : float, position x to estimate y.   Returns: float.
Pine Script™ library
by RicardoSantos
10