The volume of time series stream data grows rapidly in various applications. To reduce the storage, transmission and processing costs of time series data, segmentation and approximation is a common approach. In this paper, we propose a novel online segmentation algorithm that approximates time series by a set of different types of candidate functions (polynomials of different orders, exponential functions, etc.) and adaptively chooses the most compact one as the pattern of the time series changes. We call this algorithm the Adaptive Approximation (AA) algorithm. The AA algorithm incrementally narrows the feasible coefficient spaces (FCS) of candidate functions in coefficient coordinate systems to make each segment as long as possible given an error bound on each data point. We propose an algorithm called the FCS algorithm for the incremental computation of the feasible coefficient spaces. We further propose a mapping based index for similarity searches on the approximated time series. Experimental results show that our AA algorithm generates more compact approximations of the time series with lower average errors than the state-of-the-art algorithm, and our indexing method processes similarity searches on the approximated time series efficiently.
The Adaptive Approximation (AA) algorithm REF fits models in parallel and creates segments as each model fails.
Indexable online time series segmentation with error bound guarantee
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