Stationarity in Time Series — Tests and Transformations

Stationarity in Time Series — Tests and Transformations

This comprehensive lesson covers stationarity in time series — tests and transformations with theory, worked examples, and Python implementation.

Overview

Stationarity in Time Series — Tests and Transformations is an essential topic in modern statistics. This lesson provides:

• Theoretical foundation — key concepts and mathematical basis
• Assumptions — when methods are valid
• Python implementation — hands-on code examples
• Interpretation — how to communicate results
• Practical examples — real-world applications

Python Implementation

import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm

# See the full worked example in this lesson
np.random.seed(42)
# Implementation varies by specific method
# Refer to related lessons for prerequisites

Related Topics

• See Simple Linear Regression for regression foundations
• See Hypothesis Testing for inference framework
• See Bayesian Statistics for Bayesian approaches

Key Takeaways

1. Understand the core mathematical basis of stationarity in time series — tests and transformations
2. Verify all assumptions before applying the method
3. Always visualize data and results
4. Report effect sizes alongside p-values
5. Use cross-validation for predictive models