Speaker:Daoping Wu

Modeling Piece-Wise Stationary Time Series

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.

Committee: Christopher Quinn (major professor), Pavan Aduri, Forrest Bao, Hongyang Gao, and Qing Li

Join on Zoom: https://iastate.zoom.us/j/92277091519 Or, go to https://iastate.zoom.us/join and enter meeting ID: 922 7709 1519