Using derivatives in time series classification
PBN-AR
Instytucja
Wydział Matematyki i Informatyki (Uniwersytet im. Adama Mickiewicza w Poznaniu)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
DATA MINING AND KNOWLEDGE DISCOVERY
ISSN
1384-5810
EISSN
1573-756X
Wydawca
SPRINGER
DOI
URL
Rok publikacji
2013
Numer zeszytu
2
Strony od-do
310-331
Numer tomu
26
Identyfikator DOI
Liczba arkuszy
1.50
Autorzy
Słowa kluczowe
en
Dynamic Time Warping
Derivative Dynamic Time Warping
Data mining
Time series
Streszczenia
Język
en
Treść
Over recent years the popularity of time series has soared. Given the widespread use of modern information technology, a large number of time series may be collected during business, medical or biological operations, for example. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data, which in turn has resulted in a large number of works introducing new methodologies for indexing, classification, clustering and approximation of time series. In particular, many new distance measures between time series have been introduced. In this paper, we propose a new distance function based on a derivative. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than point-to-point function comparison. The new distance is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 20 time series datasets from a wide variety of application domains. Our experiments show that our method provides a higher quality of classification on most of the examined datasets.
Cechy publikacji
discipline:Informatyka – dziedzina nauk technicznych
discipline:Computer science – field of technical sciences
Original article
Original article presents the results of original research or experiment.
Oryginalny artykuł naukowy
Oryginalny artykuł naukowy przedstawia rezultaty oryginalnych badań naukowych lub eksperymentu.
Inne
System-identifier
PBN-R:311274