Download code through: https://github.com/weiyadi/dlm_bnn
Download code through: https://github.com/Zhennan-Wu/AISPFS
Download code through: https://github.com/weiyadi/dlm_sgp
Download code through: https://github.com/hcui01/SNAP
Download code through: https://github.com/hcui01/SOGBOFA
Download code through: https://github.com/hcui01/AGS
Download code through:: https://github.com/rsheth80
Download code through: https://github.com/rsheth80
The data was generated and kindly provided by other researchers. Please see the official OGLE site for more information on the survey, data and discoveries. Various queries on this and other astronomy data can also be made there as well as at the visier site and Harvard time series site. If using this data please cite the original work (Szymanski, 2005, Acta Astron., 55, 43 and Udalski, Kubiak and Szymanski, 1997, Acta Astron., 47, 319.) as suggested on the OGLE site.
We provide the OGLE-II dataset in order to make it more readily accessible to machine learning researchers. Toward that we packaged three versions of the data, as linked below. Please consult our papers for more information about the data, its processing and experiments.
(1) The raw data: the original time series that are measured at irregular time points and are not folded. We also provide the folded versions, as well as the known period (and other properties) as found by the OGLE project: ogle2full.tar.gz
(2) A processed form of the data: each time series is folded according to its known period, and then re-sampled via interpolation at 50 regular sampling time points. Two versions are provided, the time series "as is" and after "universal phasing". This form of the data can be simply treated as a point in 50-D Euclidean space and used directly by machine learning algorithms - providing an easy starting point to study the data. ogle50.libsvm and upogle50.libsvm