temporal_laplacian: Compute the temporal Laplacian matrix of a time series

In bbuchsbaum/graphweights: Constructing Adjacency Matrices Based on Spatial and Feature Similarity

Compute the temporal Laplacian matrix of a time series

Description

This function computes the temporal Laplacian matrix of a given time series using a specified weight mode, sigma, and window size.

Usage

temporal_laplacian(
  time,
  weight_mode = c("heat", "binary"),
  sigma = 1,
  window = 2
)

Arguments

time	A numeric vector representing a time series
weight_mode	Character, the mode for computing weights, either "heat" or "binary" (default is "heat")
sigma	Numeric, the sigma parameter for the heat kernel (default is 1)
window	Integer, the window size for computing adjacency (default is 2)

Value

A sparse symmetric matrix representing the computed temporal Laplacian

Examples

# Create an example time series
time <- 1:10

# Compute the temporal Laplacian matrix using the heat weight mode
result <- temporal_laplacian(time, weight_mode = "heat", sigma = 1, window = 2)

bbuchsbaum/graphweights documentation built on April 4, 2024, 7:19 p.m.