Online segmentation of time series based on polynomial least-squares approximations.

The paper presents SwiftSeg, a novel technique for online time series segmentation and piecewise polynomial representation. The segmentation approach is based on a least-squares approximation of time series in sliding and/or growing time windows utilizing a basis of orthogonal polynomials. This allows the definition of fast update steps for the approximating polynomial, where the computational effort depends only on the degree of the approximating polynomial and not on the length of the time window.

Processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks.

Neural networks are often used to process temporal information, i.e., any kind of information related to time series.