Automation of intervention time series analysis with R

This paper proposes an automated intervention analysis algorithm to assess the impact of external events on time series data. The proposed method automatically searches for optimal combinations of intervention functions without requiring prior specification by the analyst, thereby reducing subjectivity in model construction. The algorithm systematically evaluates candidate models and selects the best-fitting one based on commonly used evaluation criteria such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE). This automated procedure enables empirical and reproducible analysis while alleviating the need for expert knowledge of intervention modeling and time series theory. As a result, the proposed framework provides a user-friendly analytical environment even for non-experts unfamiliar with time series