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Identify Patterns That Have Become Dormant Over Time

Source: R/hibernate.R
hibernate.Rd

Analyzes time series or sequential data to identify patterns that were once active but have become dormant. This is the inverse problem from dormancy_detect - finding patterns that "went to sleep" rather than patterns that are currently dormant.

Usage

hibernate(
  data,
  time_var = NULL,
  window_size = 0.2,
  threshold = 0.3,
  min_observations = 30,
  verbose = FALSE
)

Arguments

data

A data frame with a time column or sequential index.

time_var

Character. Name of the time/sequence variable. If NULL, uses row order as sequence. Default is NULL.

window_size

Integer or numeric. Size of the rolling window for detecting changes. If integer, uses number of observations. If numeric < 1, uses proportion of data. Default is 0.2 (20% of data).

threshold

Numeric. Minimum change in pattern strength to be considered hibernation. Default is 0.3.

min_observations

Integer. Minimum observations required for analysis. Default is 30.

verbose

Logical. Whether to print progress messages. Default is FALSE.

Value

A list containing:

  • hibernated_patterns - Patterns that have become dormant

  • timeline - When patterns transitioned to dormancy

  • hibernation_depth - How deeply patterns have hibernated

  • revival_potential - Likelihood patterns could reawaken

Details

Hibernation detection is important for:

  • Understanding system evolution and regime changes

  • Identifying lost relationships that might return

  • Detecting structural breaks in relationships

  • Monitoring degradation of system components

A pattern is considered to have "hibernated" if:

  1. It was strong in an earlier time window

  2. It has weakened significantly in recent windows

  3. The weakening is not due to noise or reduced sample size

Examples

set.seed(42)
n <- 500
time <- 1:n
x <- rnorm(n)
# Relationship that fades over time
effect_strength <- exp(-time / 200)
y <- effect_strength * 0.8 * x + (1 - effect_strength) * rnorm(n)
data <- data.frame(time = time, x = x, y = y)

hib <- hibernate(data, time_var = "time", window_size = 0.15)
print(hib)
#> Pattern Hibernation Analysis
#> ============================
#> 
#> Window size: 75 
#> Hibernation threshold: 0.3 
#> 
#> Hibernated Patterns:
#>     variable_1 variable_2 early_correlation late_correlation correlation_change
#> x~y          x          y         0.9574825      -0.05163983          0.9058426
#>     hibernation_time hibernation_depth
#> x~y              258         0.9058426
#> 
#> Revival Potential:
#>   variable_pair revival_potential revival_difficulty
#> 1           x~y        0.09415736          Difficult