This package provides a Regular Vine integration for the WEKA workbench.
It uses several copula functions (see here) and a sequential structure selection with pairwise maximum likelihood estimation as copula fitting method.
Notes:
The model returns a pseudo log-likelihood (sum and average) without respecting the marginal densities. Which means, that this model is only comparable to other vine models, not to other density models in general.
Vines can only handle normalized data. So make sure your data is normalized before using. (You might use the WEKA preprocessing functions to do so.)
This work was supported by a fellowship within the FITweltweit programme of the German Academic Exchange Service (DAAD).
To (re-)build the package an installation of Apache Ant is required.
Navigate Console into the vines package and use the following ant command to build the package:
ant -f build_package.xml -Dpackage=vines-1.0.1 make_package
The .zip is placed inside the dist folder.
Use the WEKA Package Manager
You can run the package via console using:
java -cp (path)/weka.jar weka.Run RegularVine
Or use the RVine Panel inside the WEKA Explorer.
Dissmann, J., Brechmann, E. C., Czado, C., & Kurowicka, D. Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59:52-69,2013.
Song, P. X.-K. Multivariate dispersion models generated from gaussian copula. Scandinavian Journal of Statistics, 27(2):305–320, 2000.
Aas, K., Czado, C., Frigessi, A. and Bakken, H. Pair-copula constructions of multiple dependence. Insurance Mathematics and Economics, 44(2):182–198, 2009.
Fang, H. B., Fang, K. T., and Kotz, S. The meta-elliptical distributions with given marginals. Journal of Multivariate Analysis, 82(1):1–16, 2002.
Genest, C., and MacKay, J. The joy of copulas: bivariate distributions with uniform marginals. The American Statistician, 40(4):280–283, 1986.
Genest, C., and Favre, A. C. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of hydrologic engineering, 12(4):347–368, 2007.
Mahfoud, M., and Michael, M. Bivariate archimedean copulas: an application to two stock market indices. BMI Paper, 2012.
Genest, C., Masiello, E., Tribouley, K. Estimating copula densities through wavelets. Insurance: Mathematics and Economics, 44(2):170–181, 2009.
Genest, C., Kojadinovic, I., Nešlehová˛, J., and Yan, J. A goodness-of-fit test for bivariate extreme-value copulas. Bernoulli, 17(1):253–275, 2011.
HU Berlin. Multivariate Time Series. link [Online; accessed 19-June-2017].
Schirmacher, D., and Schirmacher, E. Multivariate dependence modeling using pair-copulas. Technical report, pages 14–16, 2008.
Doyon, G. On densities of extreme value copulas. M.Sc. Thesis, ETH Zurich, 2013.
Panagiotelis, A., Czado, C., Joe, H., & Stöber, J. Model selection for discrete regular vine copulas. Computational Statistics & Data Analysis, 106:138-152, 2017.