A computationally efficient method for identification of steady state in time series data from ship monitoring

Most applied time series are non-stationary, or exhibit some kind of non-stationarity for at least parts of the time series. For time series analyses or mathematical modeling purposes, the non-stationarities can be difficult to handle. Therefore, identification of stationary and non-stationary behavior is of great practical interest in time series analysis. In this study a robust and computationally efficient method to identify steady state parts of time series data is presented. The method is based on the class of deterministic trend models using a sliding window, and is focused towards being easy to implement, efficient and practical in use and to preserve data completeness. To demonstrate the performance of the steady state identifier, the method is applied on different sets of time series data from two ships equipped with systems for in-service monitoring. The method is shown to be reliable and practical for identifying steady state parts of time series data, and can serve as a practical preprocessing tool for time series data analysis.