For the last several decades, autoregressive-integrated moving average (ARIMA) modeling has been a dominant paradigm for statistical analysis of time series data.  In the recent years, however, the analyses based on a larger class of models, the linear state space models (SSMs), have gained popularity.  This shift is mainly spurred by the vastly improved modeling flexibility offered by SSMs, and the ready availability of easy-to-use software for SSM-based modeling.  The SSM and UCM procedures in SAS/ETS® and the CSSM procedure in VIYA/ECONOMETRICS® provide state-of-the-art tools for SSM-based analysis of a wide variety of sequential data types, which include univariate and multivariate time series, panels of univariate and multivariate time series, and data that are generated by multi-level, multi-subject, longitudinal studies.  This presentation provides an overview of the modeling capabilities of these procedures.  Without assuming familiarity with the SSMs, the modeling techniques are introduced using easy-to-follow, real-life examples and useful references…