time series stationary python
import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.stattools import kpss, adfuller from statsmodels.graphics.tsaplots import plot_acf, plot_pacf #https://www.kaggle.com/datasets/camnugent/sandp500
df = pd.read_csv('/content/all_stocks_5yr.csv') df.head(10) 
company = 'AAPL' company_data = df[df['Name'] == company].copy() company_data['date'] = pd.to_datetime(company_data['date']) company_data.set_index('date', inplace=True) # Plot the original time series plt.figure(figsize=(8, 6)) plt.plot(company_data['close'], label='Original Time Series') plt.title(f'{company} Closing Prices') plt.xlabel('Date') plt.ylabel('Closing Price (USD)') plt.legend() plt.show() 
# Log Transform company_data['Log_Close'] = np.log(company_data['close']) plt.figure(figsize=(8, 6)) plt.plot(company_data['Log_Close'], label='Log Transform') plt.title(f'{company} Log-Transformed Closing Prices') plt.xlabel('Date') plt.ylabel('Log of Closing Price') plt.legend() plt.show() 
# Differencing company_data['Diff_Close'] = company_data['close'].diff() plt.figure(figsize=(8, 6)) plt.plot(company_data['Diff_Close'], label='Differencing') plt.title(f'{company} First-Order Differencing of Closing Prices') plt.xlabel('Date') plt.ylabel('Differenced Closing Price') plt.legend() plt.show() 
# Log Transform followed by Differencing company_data['Log_Diff_Close'] = company_data['Log_Close'].diff() plt.figure(figsize=(8, 6)) plt.plot(company_data['Log_Diff_Close'], label='Log Transform + Differencing') plt.title(f'{company} Log Transform followed by Differencing of Closing Prices') plt.xlabel('Date') plt.ylabel('Differenced Log of Closing Price') plt.legend() plt.show() 
# Remove null values resulting from differencing company_data.dropna(inplace=True) adfuller(company_data['close']) # Function to run ADF Test and print results def adf_test(series): result = adfuller(series) print(f'p-value: {result[1]}') if result[1] < 0.05: print("Conclusion: The series is stationary (Reject H0).") else: print("Conclusion: The series is non-stationary (Fail to reject H0).") print('\n') # Run ADF test on original data adf_test(company_data['close'], 'ADF Test on Original Closing Prices') 
# Run ADF test on log-transformed data adf_test(company_data['Log_Close'], 'ADF Test on Log-Transformed Closing Prices') 
# Run ADF test on first-differenced data adf_test(company_data['Diff_Close'], 'ADF Test on Differenced Closing Prices') 
# Run ADF test on log + differenced data adf_test(company_data['Log_Diff_Close'], 'ADF Test on Log-Differenced Closing Prices') 
def kpss_test(series, title): print(f'--- {title} ---') result = kpss(series, regression='c', nlags="auto") print(f'p-value: {result[1]}') if result[1] < 0.05: print("Conclusion: The series is not stationary (Reject H0).") else: print("Conclusion: The series is stationary (Fail to reject H0).") print('\n') kpss_test(company_data['close'], "Original Series") 
kpss_test(company_data['Log_Close'], "Log-Transformed Series") 
kpss_test(company_data['Diff_Close'], "First-Differenced Series") 
kpss_test(company_data['Log_Diff_Close'], "Log-Transformed + Differenced Series") 
# Plot ACF and PACF for the Original Series fig, axes = plt.subplots(4, 2, figsize=(10, 14)) plot_acf(company_data['close'], ax=axes[0, 0], title="ACF - Original Series") plot_pacf(company_data['close'], ax=axes[0, 1], title="PACF - Original Series") # Plot ACF and PACF for the Log-Transformed Series plot_acf(company_data['Log_Close'], ax=axes[1, 0], title="ACF - Log-Transformed Series") plot_pacf(company_data['Log_Close'], ax=axes[1, 1], title="PACF - Log-Transformed Series") # Plot ACF and PACF for the First-Differenced Series plot_acf(company_data['Diff_Close'], ax=axes[2, 0], title="ACF - First-Differenced Series") plot_pacf(company_data['Diff_Close'], ax=axes[2, 1], title="PACF - First-Differenced Series") # Plot ACF and PACF for Log-Transformed + Differenced Series plot_acf(company_data['Log_Diff_Close'], ax=axes[3, 0], title="ACF - Log-Transformed + Differenced Series") plot_pacf(company_data['Log_Diff_Close'], ax=axes[3, 1], title="PACF - Log-Transformed + Differenced Series") # Layout adjustment for better visualization plt.tight_layout() plt.show() 
