In this talk, Ali Rahimi of Intel Research describes a semi-supervised regression algorithm that learns to transform one time series into another time series. Rahimi applies this algorithm to tracking, where one transforms a time series of observations from sensors to a time series describing the pose of a target.  Rahimi suggests learning a memoryless transformation of time series from a few example input-output mappings. This algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. From the Series:CSE Colloquia - 2006