Online Amnesic Approximation of Streaming Time Series

The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real time sensors has brought home the need for representations that can be incrementally updated, and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains recent information is more useful than older information. We call such representations amnesic. While there has been previous work on amnesic representations, the class of amnesic functions possible was dictated by the representation itself. In this work, we introduce a novel representation of time series that can ...

• Type: Conference
• Year: 2004
• Where: ICDE
• Authors: Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel
• Keywords: Amnesic Representations, Database, Exponential Amnesic Function, ICDE 2004, User-specified Amnesic Functions