Segmenting time series with connected lines under maximum error bound
The error-bounded Piecewise Linear Approximation (PLA) is to approximate the stream data by lines such that the approximation error at each point does not exceed a pre-defined error. In this paper, we focus on the version of PLA problem that generates connected lines in the segmentation for smooth approximation. We provide a new linear-time algorithm for the problem that outperform two of the existing methods with less number of connected segments. Our extensive experiments, on both real and synthetic data sets, indicate that our proposed algorithms are practically efficient.
piecewise linear approximation/representation time series data haar wavelet data compression