Stationarity is a crucial concept in time series analysis, and ensuring data stationarity is essential for accurate modeling and forecasting. This section will demonstrate Stationarity in Practice (Code Implementation) using Python code.
Checking for Stationarity Using the Augmented Dickey-Fuller (ADF) Test
The Augmented Dickey-Fuller (ADF) test is a popular statistical test for checking stationarity. It tests the null hypothesis that a unit root exists in the time series. If the null hypothesis is rejected, the series is considered stationary.
Python
import pandas as pd
from statsmodels.tsa.stattools import adfuller
# Load the data
data = pd.read_csv('data.csv', index_col='Date')
# Perform the ADF test
adf_result = adfuller(data['Value'])
# Print the results
print('ADF Statistic:', adf_result[0])
print('p-value:', adf_result[1])
print('Critical Values:')
for key, value in adf_result[4].items():
print('\t%s: %.3f' % (key, value))
Making Data Stationary Using Differencing
If the ADF test indicates that the data is non-stationary, differencing can be used to make it stationary. Differencing involves subtracting the previous value from the current value.
Python
import pandas as pd
from statsmodels.tsa.stattools import adfuller
# Load the data
data = pd.read_csv('data.csv', index_col='Date')
# First-order differencing
differenced_data = data['Value'].diff().dropna()
# Perform the ADF test on the differenced data
adf_result = adfuller(differenced_data)
# Print the results
print('ADF Statistic:', adf_result[0])
print('p-value:', adf_result[1])
print('Critical Values:')
for key, value in adf_result[4].items():
print('\t%s: %.3f' % (key, value))
Visualizing Stationarity
Visual inspection can also be helpful in assessing stationarity. Plot the time series and look for trends, seasonality, and other non-stationary patterns. If the data appears to be trending or has a clear seasonal pattern, it is likely non-stationary.
