Title: Efficient and Robust Polylinear Analysis of Noisy Time Series,
Abstract: A method is proposed to generate an optimal fit of a number of connected
linear trend segments onto time-series data. To be able to efficiently handle
many lines, the method employs a stochastic search procedure to determine
optimal transition point locations. Traditional methods use exhaustive grid
searches, which severely limit the scale of the problems for which they can be
utilized. The proposed approach is tried against time series with severe noise
to demonstrate its robustness, and then it is applied to real medical data as
an illustrative example.