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Analyzes pattern causality relationships between multiple time series by computing pairwise causality measures and organizing them into matrices.

Usage

pcMatrix(
  dataset,
  E,
  tau,
  metric = "euclidean",
  h,
  weighted = TRUE,
  distance_fn = NULL,
  state_space_fn = NULL,
  verbose = FALSE,
  n_cores = 1
)

Arguments

dataset

Matrix or data frame of time series

E

Integer; embedding dimension

tau

Integer; time delay

metric

Character; distance metric ("euclidean", "manhattan", "maximum")

h

Integer; prediction horizon

weighted

Logical; whether to use weighted causality

distance_fn

Optional custom distance function

state_space_fn

Optional custom state space reconstruction function

verbose

Logical; whether to print progress

n_cores

Integer; number of cores for parallel computation

Value

A pc_matrix object containing causality matrices

Details

Compute Pattern Causality Matrix Analysis

The function performs these key steps:

  • Validates input data and parameters

  • Computes pairwise causality measures

  • Organizes results into causality matrices

  • Provides summary statistics for each causality type

  • vars: Vector autoregression analysis

  • tseries: Time series analysis tools

  • forecast: Time series forecasting methods