Time series modeling remains a central methodological tool across economics, finance, and applied data science, particularly where forecasting and inference are required under complex data-generating processes. A persistent challenge in such analyses is the presence of non-stationarity, skewness, heteroskedasticity, and structural irregularities that violate classical regression assumptions. This study develops a comprehensive, integrated framework that combines data transformation techniques, stationarity assessment, and regression-based time series modeling within a coherent methodological pipeline. Drawing on foundational contributions in exploratory data analysis, transformation theory, unit root testing, and dynamic regression, the paper synthesizes classical econometric methods with insights from modern statistical learning. Emphasis is placed on power transformations, including Box–Cox and Yeo–Johnson families, as tools for improving distributional properties and model stability. The framework further incorporates both conventional and alternative approaches to integration order testing, situating them within a broader diagnostic strategy. Using macroeconomic time series as a motivating context, the study demonstrates how careful preprocessing and transformation influence parameter interpretability, forecast accuracy, and inferential robustness. The discussion highlights methodological trade-offs, limitations of standard unit root tests, and the implications of transformation choices for regression diagnostics. By offering a structured approach that bridges traditional econometrics and contemporary data preprocessing practices, this paper contributes to a more transparent and replicable foundation for regression-based time series analysis.

Time series analysis, data transformation, stationarity testing, regression modeling, unit root diagnostics, econometric forecasting

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