Journal of Intelligent Information Systems, 42(3), 531–566p. (2014) DOI:10.1007/s10844-013-0290-3

TS-stream: clustering time series on data streams

C. M. M. Pereira, R. F. de Mello

The current ability to produce massive amounts of data and the impossibility in storing it motivated the development of data stream mining strategies. Despite the proposal of many techniques, this research area still lacks in approaches to mine data streams composed of multiple time series, which has applications in finance, medicine and science. Most of the current techniques for clustering streaming time series have a serious limitation in their similarity measure, which are based on the Pearson correlation. In this spaper, we show the Pearson correlation is not capable of detecting similarities even for classic time series models, such as those by Box and Jenkins. This limitation motivated our proposal to cluster streaming time series based on their generating functions, which is achieved by considering features obtained using descriptive measures, such as Auto Mutual Information, the Hurst Exponent and several others. We present a new tree-based clustering algorithm, entitled TS-Stream, which uses the extracted features to produce partitions in better accordance to the time series generating functions. Experiments with synthetic data sets confirm TS-Stream outperforms ODAC, currently the most popular technique, in terms of clustering quality. Using real financial time series from the NYSE and NASDAQ, we conducted stock trading simulations employing TS-Stream to support the creation of diversified investment portfolios. Results confirmed TS-Stream increased the monetary returns in several orders of magnitude when compared to trading strategies simply based on the Moving Average Convergence Divergence financial indicator.

back


Creative Commons License © 2017 SOME RIGHTS RESERVED
The content of this web site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Germany License.

Please note: The abstracts of the bibliography database may underly other copyrights.

Ihr Browser versucht gerade eine Seite aus dem sogenannten Internet auszudrucken. Das Internet ist ein weltweites Netzwerk von Computern, das den Menschen ganz neue Möglichkeiten der Kommunikation bietet.

Da Politiker im Regelfall von neuen Dingen nichts verstehen, halten wir es für notwendig, sie davor zu schützen. Dies ist im beidseitigen Interesse, da unnötige Angstzustände bei Ihnen verhindert werden, ebenso wie es uns vor profilierungs- und machtsüchtigen Politikern schützt.

Sollten Sie der Meinung sein, dass Sie diese Internetseite dennoch sehen sollten, so können Sie jederzeit durch normalen Gebrauch eines Internetbrowsers darauf zugreifen. Dazu sind aber minimale Computerkenntnisse erforderlich. Sollten Sie diese nicht haben, vergessen Sie einfach dieses Internet und lassen uns in Ruhe.

Die Umgehung dieser Ausdrucksperre ist nach §95a UrhG verboten.

Mehr Informationen unter www.politiker-stopp.de.