An adaptive algorithm for online time series segmentation with error bound guarantee
Zhenghua Xu, Rui Zhang, Kotagiri Ramamohanarao, Udaya Parampalli · 2012
The volume of time series data grows rapidly in various applications such as network traffic management, telecommunications, finance and sensor network. To reduce the cost of storage, transmission and processing of time series data, the need for more compact representations of time series data is compelling. Segmentation is one of the most commonly used methods to meet this requirement. Both PLA and PPA are common segmentation methods which divide a time series into segments and use a linear function or a polynomial function to approximate each segment, respectively. However, while most of the current PLA and PPA methods aim to minimize the holistic error between the approximation and the original time series, few works try to represent time series as compact as possible with an error bound guarantee on each data point. Furthermore, in many real world situations, the patterns of the time series do not follow a constant rule such that using only one type of functions may not yield the best compaction.