Calculate Full Details Pattern Causality Analysis

Implements an advanced pattern causality algorithm to explore the causal relationships between two time series datasets. This function provides comprehensive analysis of causality patterns, including state space reconstruction, pattern identification, and causality strength evaluation.

Usage

pcFullDetails(
  X,
  Y,
  E,
  tau,
  h,
  weighted,
  metric = "euclidean",
  distance_fn = NULL,
  state_space_fn = NULL,
  verbose = FALSE
)

Arguments

X

Numeric vector; the first time series data

Y

Numeric vector; the second time series data

E

Integer; embedding dimension for state space reconstruction

tau

Integer; time delay between data points

h

Integer; prediction horizon for causality analysis

weighted

Logical; whether to weight causality strength

metric

Character; distance metric ('euclidean', 'manhattan', or 'maximum')

distance_fn

Optional custom distance function for computing distances (default: NULL)

state_space_fn

Optional custom function for state space reconstruction (default: NULL)

verbose

Logical; if TRUE, prints computation progress (default: FALSE)

Value

A pc_full_details object containing:

• backtest_time: Time points used for backtesting

• valid_time: Valid time points for analysis

• causality_real: Real causality spectrum

• causality_pred: Predicted causality spectrum

• state_spaces: State space reconstructions

• neighbors: Nearest neighbor information

• patterns: Pattern and signature information

• matrices: Causality matrices

• predictions: Predicted and actual values

• weighted: A logical indicating if weighted calculations were used

• E: Embedding dimension used for the analysis

Details

Calculate Full Details Pattern Causality Analysis

The function implements these key steps:

• State Space Reconstruction: Creates shadow attractors using embedding

• Pattern Analysis: Converts time series into signature and pattern spaces

• Nearest Neighbor Analysis: Identifies and analyzes local dynamics

• Causality Evaluation: Computes predicted and actual causality matrices

• Results Validation: Provides detailed diagnostics and quality metrics