Time Series Stationarity: KPSS and Phillips-Perron Tests in Python

Raw time series data often exhibits trends and seasonality that can skew your forecasting models and lead to spurious statistical results. To build reliable predictive models, you must first master the mathematical foundations and practical application of stationarity testing. This text-only course guides you through the essential concepts of stationarity, focusing on two critical statistical methods: the KPSS and Phillips-Perron tests. By completing this program, you will understand how to formulate hypotheses, interpret test statistics, and confidently apply these tests to prepare your datasets for downstream machine learning and econometric forecasting models. What you'll learn: - Understand the fundamental concept of stationarity and why it is crucial for time series analysis. - Formulate and interpret the null and alternative hypotheses for both the KPSS and Phillips-Perron tests. - Compare the strengths and limitations of KPSS and Phillips-Perron tests against traditional unit root tests. - Implement statistical tests in Python using modern libraries like pandas and statsmodels. - Analyze test outputs, including test statistics, critical values, and p-values, to make confident data decisions. - Apply differencing and transformation techniques to convert non-stationary data into stationary series. The course begins with core definitions and the theoretical foundations of stationarity before moving on to clear, step-by-step code demonstrations and written exercises. You will walk through real-world scenarios, learning how to combine multiple tests to resolve conflicting statistical results. This course is designed for beginner data analysts, aspiring data scientists, and programmers who want to strengthen their time series analysis skills. A basic familiarity with Python is helpful, but no prior background in advanced econometrics is required. Start reading today to master the essential testing techniques for robust time series forecasting.