TY - GEN
T1 - Copula in temporal data mining
T2 - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012
AU - Fan, Bihang
AU - Guo, Li
AU - Li, Ning
PY - 2012
Y1 - 2012
N2 - Copula has become a popular tool in multivariate modeling widely applied in lots of fields, but less used in temporal data. The analysis of the extreme temperature is an important part of the study in climate change, and the data of extreme temperature is one of the temporal data. So in this study, copula is used to calculate the joint return period of extreme temperature (from station in Beijing) with the indices Frost Days (FD and Summer Days (SU35). We used Anderson-Darling goodness-of-fit test (A-D test) to find the most fitted probability distribution and evaluate the 10-year return period, 50-year return period and 100-year return period based on the marginal distribution of the two univariate. After calculating the joint return period, we compared the results of univariate return period and joint return period with the reality. The results show that, the joint return period is more accurate than the univariate period, and by improving both the choice of indices and the copula method, the results should closer to the reality. This study is of significance to get a better understanding in temporal data mining by using copula method.
AB - Copula has become a popular tool in multivariate modeling widely applied in lots of fields, but less used in temporal data. The analysis of the extreme temperature is an important part of the study in climate change, and the data of extreme temperature is one of the temporal data. So in this study, copula is used to calculate the joint return period of extreme temperature (from station in Beijing) with the indices Frost Days (FD and Summer Days (SU35). We used Anderson-Darling goodness-of-fit test (A-D test) to find the most fitted probability distribution and evaluate the 10-year return period, 50-year return period and 100-year return period based on the marginal distribution of the two univariate. After calculating the joint return period, we compared the results of univariate return period and joint return period with the reality. The results show that, the joint return period is more accurate than the univariate period, and by improving both the choice of indices and the copula method, the results should closer to the reality. This study is of significance to get a better understanding in temporal data mining by using copula method.
UR - http://www.scopus.com/inward/record.url?scp=84881020251&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881020251&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84881020251
SN - 9788994364193
T3 - Proceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012
SP - 592
EP - 597
BT - Proceedings - 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (NISS, ICMIA and NASNIT), ISSDM 2012
Y2 - 23 October 2012 through 25 October 2012
ER -
