Name
Abstract | ||
|---|---|---|
SAX (Symbolic Aggregate approXimation) is one of the main symbolization techniques for time series. A well-known limitation of SAX is that trends are not taken into account in the symbolization. This paper proposes 1d-SAX a method to represent a time series as a sequence of symbols that each contain information about the average and the trend of the series on a segment. We compare the efficiency of SAX and 1d-SAX in terms of goodness-of-fit, retrieval and classification performance for querying a time series database with an asymmetric scheme. The results show that 1d-SAX improves performance using equal quantity of information, especially when the compression rate increases. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-41398-8_24 | ADVANCES IN INTELLIGENT DATA ANALYSIS XII |
Keywords | Field | DocType |
sax | Data mining,Data compression ratio,Symbolic aggregate approximation,Computer science,Artificial intelligence,Time series database,Machine learning | Conference |
Volume | ISSN | Citations |
8207 | 0302-9743 | 11 |
PageRank | References | Authors |
0.61 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Malinowski | 1 | 74 | 12.46 |
| Thomas Guyet | 2 | 100 | 15.98 |
| René Quiniou | 3 | 100 | 14.23 |
| Romain Tavenard | 4 | 159 | 16.16 |