A symbolic representation of time series, with implications for streaming algorithms

The parallel explosions of interest in streaming data, and data mining of time series have had surprisingly little intersection. This is in spite of the fact that time series data are typically streaming data. The main reason for this apparent paradox is the fact that the vast majority of work on streaming data explicitly assumes that the data is discrete, whereas the vast majority of time series data is real valued. Many researchers have also considered transforming real valued time series into symbolic representations, noting that such representations would potentially allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities, in addition to allowing formerly “batch-only” problems to be tackled by the streaming community. While many symbolic representations of time series have been introduced over the past decades, they all suffer from three fatal flaws. Firstly, the dimensionality of the symbolic represent...

• Type: Conference
• Year: 2003
• Where: DMKD
• Authors: Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu
• Keywords: DMKD 2003, Streaming Data, Symbolic Representation, Time Series