Modeling Piece-Wise Stationary Time Series

Daoping Wu, Suhas Gundimeda, Shaoshuai Mou, Christopher J. Quinn

Abstract: We consider the problem of modeling piece-wise stationary time series. We propose a new, data-driven technique to automatically identify change-points and learn piece-wise stationary models. We do not assume prior knowledge of the stationary models or the number of change points. Our method can automatically identify repeated stationary models. Our method employs sliding windows and clustering in a novel way. We use the minimum description length principle and integer linear programming to identify the lowest overall complexity system model. Our method does not require parameter tuning and leads to good segmentation and compression. We demonstrate the effectiveness of our method against traditional techniques using both simulated and real-world data.