hibernate: Identify Patterns That Have Become Dormant Over Time

In dormancy: Detection and Analysis of Dormant Patterns in Data

Identify Patterns That Have Become Dormant Over Time

Description

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.

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

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

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)

dormancy documentation built on March 16, 2026, 5:09 p.m.