KPSS-test
#import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.stattools import kpss # Generate synthetic stationary and non-stationary data np.random.seed(17) # Stationary data: White noise stationary_data = np.random.normal(size=100) # Create a random walk with larger step size to make it more volatile random_walk = np.cumsum(np.random.normal(scale=2, size=n)) # Increase the scale for larger volatility trend = np.linspace(0, 30, n) # Add a strong linear trend (larger trend) # Combine random walk and trend for clear non-stationary behavior non_stationary_data = random_walk + trend # Plot the data plt.figure(figsize=(10,5)) plt.subplot(1, 2, 1) plt.plot(stationary_data) plt.title('Stationary Data') plt.subplot(1, 2, 2) plt.plot(non_stationary_data) plt.title('Non-Stationary Data') plt.tight_layout() plt.show() 
parts of the ADF test –
tuple
result = kpss(stationary_data) 
print(result) 
access by index
print('ADF Test Statistic:', result[0]) 
print('p-value:', result[1]) 
print('Number of Lags Used:', result[2]) 
print('Critical Values:', result[3]) 
adf_stat, p_value, lags, n_obs = result print(adf_stat) 
# Function to perform ADF test def kpss_test(series): result = kpss(series) print(f'p-value: {result[1]}') if result[1] <= 0.05: print("=> Strong evidence against the null hypothesis, reject the null hypothesis. Data is non-stationary.") else: print("=> Weak evidence against the null hypothesis, fail to reject the null hypothesis. Data is level or trend stationary.") kpss_test(stationary_data) 
kpss_test(non_stationary_data) 
