Name
Abstract | ||
|---|---|---|
Similarity assessment between financial time series is one of problems where the proper methodological choice is very important. The typical correlation approach can lead to misleading results. Often the similarity measure is opposite to the visual observations, expert's knowledge and even a common sense. The reasons of that can be associated with the properties of the correlation measure and its adequateness for analyzed data, as well as in terms of methodological aspects. In this article, we indicate disadvantages associated with the use of correlation to assess the similarity of financial time series and propose an alternative solution based on divergence measures. In particular, we focus on the Bose-Einstein divergence. The practical experiments conducted on simulated and real data confirmed our concept. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-37213-1_43 | ADAPTIVE AND NATURAL COMPUTING ALGORITHMS, ICANNGA 2013 |
Keywords | Field | DocType |
time series similarity,divergence measures,Bose-Einstein divergence | Similarity analysis,Divergence,Common sense,Similarity measure,Computer science,Bose–Einstein condensate,Correlation,Artificial intelligence,Finance,Machine learning | Conference |
Volume | ISSN | Citations |
7824 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryszard Szupiluk | 1 | 38 | 8.97 |
| Tomasz Zabkowski | 2 | 32 | 11.28 |