Title: Real time prediction of irregular periodic time series data
Authors: Chi Tim Ng - Chonnam National University (Korea, South) [presenting]
Abstract: By means of a novel time-dependent cumulated variation penalty function, a new class of real-time prediction methods is developed to improve the prediction accuracy of time series exhibiting irregular periodic patterns, in particular, the breathing motion data of the patients during the robotic radiation therapy. The proposed methods are designed so that real-time updates can be done efficiently with O(1) computational complexity upon the arrival of a new signal without scanning the old data repeatedly. The performances are tested via simulation under models involving abrupt changes and gradual changes in mean, trend, amplitude, and frequency.