How to Create an Interrupted Time Series Mock in Python

import numpy as np

# Mock interrupted time series data
np.random.seed(42)
n_pre = 50
n_post = 50
time = np.arange(0, n_pre + n_post)

# Pre-intervention: linear trend + noise
pre_trend = 0.05 * time[:n_pre] + np.random.normal(0, 0.5, n_pre)

# Post-intervention: new slope + level shift + noise
post_trend = 0.05 * time[:n_post] + 2.5 + 0.1 * np.arange(n_post) + np.random.normal(0, 0.5, n_post)

# Combine series
y = np.concatenate([pre_trend, post_trend])

# Intervention occurs at index 50 (just after time point 49)
intervention_time = 50

# Print first 10 and last 10 points for inspection
print("Time:", time[:10].tolist())
print("Value (first 10):", np.round(y[:10], 2).tolist())
print("Value (last 10):", np.round(y[-10:], 2).tolist())
print("Intervention at time index:", intervention_time)

# Calculate simple before/after means for quick check
mean_pre = np.mean(y[:intervention_time])
mean_post = np.mean(y[intervention_time:])
print(f"Mean pre-intervention: {mean_pre:.2f}")
print(f"Mean post-intervention: {mean_post:.2f}")
print(f"Level change: {mean_post - mean_pre:.2f}")