Uncovering delayed patterns in noisy and irregularly sampled time series: An astronomy application

Pattern Recognition

Volumn 43, Issue 3, 2010, Pages 1165-1179

  Cuevas Tello, Juan C ^a,b   Tiňo, Peter ^b   Raychaudhury, Somak ^b   Yao, Xin ^b   Harva, Markus ^c  

^a UNIVERSIDAD AUTÓNOMA DE SAN LUIS POTOSÍ   (Mexico)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^c AALTO UNIVERSITY   (Finland)

Author keywords

Evolutionary algorithms; Kernel regression; Mixed representation; Statistical analysis; Time series

Indexed keywords

ARTIFICIAL DATA; ASTRONOMICAL OBSERVATION; KERNEL REGRESSION; MIXED REPRESENTATION; MIXED TYPE; NOISY VERSIONS; OPTICAL MONITORING; STABLE TIME; STATISTICAL ANALYSIS; TIME DELAY ESTIMATION; TIME-SERIES DATA;

ASTROPHYSICS; PARAMETER ESTIMATION; TIME DELAY; TIME SERIES; TIME SERIES ANALYSIS;

EVOLUTIONARY ALGORITHMS;

EID: 70449712645     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.07.016     Document Type: Article

References (51)

1. Anderson T., and Sclove S. An Introduction to the Statistical Analysis of Data (1978), Houghton Mifflin Company
2. Arnold D., and Beyer H. A general noise model and its effects on evolution strategy performance. IEEE Transactions on Evolutionary Computation 10 4 (2006) 380-391
3. Berry D.A., and Lindgren B.W. Statistical Theory and Methods (1998), Brooks/Cole Publishing Company
4. Biethahn J., and Nissen V. Evolutionary Algorithms in Management Applications (1995), Springer, Berlin
5. E. Bonilla-Huerta, B. Duval, J.-K. Hao, A hybrid ga/svm approach for gene selection and classification of microarray data, in: Franz Rothlauf, et al. (Eds.), Applications of Evolutionary Computing, EvoWorkshops 2006, Lecutre Notes in Computer Science, vol. 3907, Springer, Berlin, 2006, pp. 34-44.
6. W. Bridewell, P. Langley, S. Racunas, S. Borrett, Learning process models with missing data, in: Machine Learning: ECML 2006, Lecture Notes in Artificial Intelligencem, vol. 4212, Springer, Berlin, 2006, pp. 557-565.
7. Burud I., Magain P., Sohy S., and Hjorth J. A novel approach for extracting time-delays from light curves of lensed quasar images. Astronomy and Astrophysics 380 2 (2001) 805-810
8. Cant-Paz E., and Goldberg D. Efficient parallel genetic algorithms: theory and practice. Computer Methods in Applied Mechanics and Engineering 186 1 (2000) 221-238
9. A.J. Chipperfield, P.J. Fleming, C.M. Fonseca, Genetic algorithm tools for control systems engineering, in: Proceedings of the First International Conference on Adaptive Computing in Engineering Design and Control, Plymouth Engineering Design Centre, UK, 1994, pp. 128-133 〈http://www.shef.ac.uk/acse/research/ecrg/getgat.html〉.
10. A.J. Chipperfield, P.J. Fleming, H. Pohlheim, C.M. Fonseca, Genetic Algorithm Toolbox for use with MATLAB, first ed., Automatic Control and Systems Engenieering, University of Sheffield, 1996 〈http://www.shef.ac.uk/acse/research/ecrg/getgat.html〉.
11. Colley W., Schild R., Abajas C., Alcalde D., Aslan Z., Bikmaev I., Chavushyan V., Chinarro L., Cournoyer J., Crowe R., Dudinov V., Evans A., Jeon Y., Goicoechea L., Golbasi O., Khamitov I., Kjernsmo K., Lee H., Lee J., Lee K., Lee M., Lopez-Cruz O., Mediavilla E., Moffat A., Mujica R., Ullan A., Munoz J., Oscoz A., Park M., Purves N., Saanum O., Sakhibullin N., Serra-Ricart M., Sinelnikov I., Stabell R., Stockton A., Teuber J., Thompson R., Woo H., and Zheleznyak A. Around-the-clock observations of the Q 0957 + 561 A, B gravitationally lensed quasar. II. Results for the second observing season. Astronomy and Astrophysics 587 1 (2003) 71-79
12. R. Cook, D. McLeish (Eds.), Workshop on Missing Data Problems, Fields Institute, Toronto, Canada, 2004 〈http://www.fields.utoronto.ca/programs/scientific/04-05/missing-data/〉.
13. Cowan G. Statistical Data Analysis (1998), Oxford University Press, Oxford
14. Cuevas-Tello J., Tiňo P., and Raychaudhury S. How accurate are the time delay estimates in gravitational lensing?. Astronomy and Astrophysics 454 (2006) 695-706
15. J. Cuevas-Tello, P. Tiňo, S. Raychaudhury, A kernel-based approach to estimating phase shifts between irregularly sampled time series: an application to gravitational lenses, in: Machine Learning: ECML 2006, Lecture Notes in Artificial Intelligence, vol. 4212, Springer, Berlin, 2006, pp. 614-621.
16. Dudewicz E.J., and Mishra S.N. Modern Mathematical Statistics (1988), Wiley, New York
17. Eigenbrod A., Courbin F., Vuissoz C., Meylan G., Saha P., and Dye S. COSMOGRAIL: the COSmological MOnitoring of GRAvItational lenses. I. How to sample the light curves of gravitationally lensed quasars to measure accurate time delays. Astronomy and Astrophysics 436 (2005) 25-35
18. Fohlmeister J., Kochanek C.S., Falco E.E., Morgan C.W., and Wambsganss J. The rewards of patience: an 822 day time delay in the gravitational lens SDSS J 1004 + 4112. Astrophysical Journal 676 (2008) 761-766
19. O. Giel, P. Lehre, On the effect of populations in evolutionary multi-objective optimization, in: Maarten Keijzer, et al. (Eds.), Genetic and Evolutionary Computation Conference (GECCO), vol. 1, ACM Press, New York, 2006, pp. 651-658.
20. Goldberg D. Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley, Reading, MA
21. Golub G., and Loan C.V. Matrix Computations. 2nd ed (1989), Johns Hopkins University Press, Baltimore, MD
22. Haarsma D., Hewitt J., Lehar J., and Burke B. The radio wavelength time delay of gravitational lens 0957 + 561. Astrophysical Journal 510 1 (1999) 64-70
23. Harva M., and Raychaudhury S. Bayesian estimation of time delays between unevenly sampled signals. Neurocomputing 72 1-3 (2008) 32-38
24. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer, Berlin
25. Howley T., and Madden M.G. The genetic kernel support vector machine: description and evaluation. Artificial Intelligence Review 24 3 (2005) 379-395
26. Inada N., Oguri M., Becker R.H., Shin M.-S., Richards G.T., Hennawi J.F., White R.L., Pindor B., Strauss M.A., Kochanek C.S., Johnston D.E., Gregg M.D., Kayo I., Eisenstein D., Hall P.B., Castander F.J., Clocchiatti A., Anderson S.F., Schneider D.P., York D.G., Lupton R., Chiu K., Kawano Y., Scranton R., Frieman J.A., Keeton C.R., Morokuma T., Rix H.-W., Turner E.L., Burles S., Brunner R.J., Sheldon E.S., Bahcall N.A., and Masataka F. The sloan digital sky survey quasar lens search. II. Statistical lens sample from the third data release. Astronomical Journal 135 (2008) 496-511
27. Jin Y., and Branke J. Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9 3 (2005) 303-317
28. Kochanek C., and Schechter P. The Hubble constant from gravitational lens time delays. In: Freedman W.L. (Ed). Measuring and Modeling the Universe, from the Carnegie Observatories Centennial Symposia vol. 2 (2004), Cambridge University Press, Cambridge 117
29. Kundic T., Turner E., Colley W., Gott III J., Rhoads J., Wang Y., Bergeron L., Gloria K., Long D., Malhorta S., and Wambsganss J. A robust determination of the time delay in 0957 + 561 A, B and a measurement of the global value of Hubble's constant. Astrophysical Journal 482 1 (1997) 75-82
30. Mitchell T. Machine Learning Computer Science (1997), McGraw-Hill, New York
31. Oguri M. Gravitational lens time delays: a statistical assessment of lens model dependences and implications for the global hubble constant. Astrophysical Journal 660 (2007) 1-15
32. Ovaldsen J., Teuber J., Schild R., and Stabell R. New aperture photometry of QSO 0957 + 561; application to time delay and microlensing. Astronomy and Astrophysics 402 3 (2003) 891-904
33. Paenke I., Branke J., and Jin Y. Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation 10 4 (2006) 405-420
34. Pelt J., Hjorth J., Refsdal S., Schild R., and Stabell R. Estimation of multiple time delays in complex gravitational lens systems. Astronomy and Astrophysics 337 3 (1998) 681-684
35. Pelt J., Kayser R., Refsdal S., and Schramm T. Time delay controversy on QSO 0957 + 561 not yet decided. Astronomy and Astrophysics 286 1 (1994) 775-785
36. Pelt J., Kayser R., Refsdal S., and Schramm T. The light curve and the time delay of QSO 0957 + 561. Astronomy and Astrophysics 305 1 (1996) 97-106
37. T. Phienthrakul, B. Kijsirikul, Evolutionary strategies for multi-scale radial basis function kernels in support vector machines, in: Hans-Georg Beyer, et al. (Eds.), Genetic and Evolutionary Computation Conference (GECCO), vol. 1, 2005, pp. 905-911.
38. Pijpers F. The determination of time delays as an inverse problem-the case of the double quasar 0957 + 561. Monthly Notices of the Royal Astronomical Society 289 4 (1997) 933-944
39. Pindor B. Discovering gravitational lenses through measurements of their time delays. Astrophysical Journal 626 (2005) 649-656
40. Press W., Rybicki G., and Hewitt J. The time delay of gravitational lens 0957 + 561, I. Methodology and analysis of optical photometric data. Astrophysical Journal 385 1 (1992) 404-415
41. Press W., Teukolsky S., Vetterling W., and Flannery B. Numerical Recipes in C ++: The Art of Scientific Computing. second ed. (2002), Cambridge University Press, Cambridge
42. Refsdal S. On the possibility of determining the distances and masses of stars from the gravitational lens effect. Monthly Notices of the Royal Astronomical Society 134 (1966) 315-319
43. Regis R., and Shoemaker C. Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Transactions on Evolutionary Computation 8 5 (2004) 490-505
44. J.E. Rowe, D. Hidovic, An evolution strategy using a continuous version of the gray-code neighbourhood distribution, in: Kalyanmoy Deb, et al. (Eds.), Genetic and Evolutionary Computation Conference (GECCO), vol. 1, Springer, Berlin, 2004, pp. 725-736.
45. P. Saha, Gravitational Lensing, in: P. Murdin (Ed.), Encyclopedia of Astronomy and Astrophysics, Nature Publishing, London, 2001.
46. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press, Cambridge
47. Spears W.M. Evolutionary Algorithms: The role of Mutation and Recombination, Natural computing (2000), Springer, Berlin
48. Tu Z., and Lu Y. A Robust Stochastic genetic Algorithm (StGA) for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation 8 (2004) 456-470
49. Vuissoz C., Courbin F., Sluse D., Meylan G., Chantry V., Eulaers E., Morgan C., Eyler M.E., Kochanek C.S., Coles J., Saha P., Magain P., and Falco E.E. COSMOGRAIL: the COSmological MOnitoring of GRAvItational Lenses. VII. Time delays and the Hubble constant from WFI J2033-4723. Astronomy and Astrophysics 488 (2008) 481-490
50. Walsh D., Carswell R.F., and Weymann R.J. 0957 + 561 A, B-twin quasistellar objects or gravitational lens. Nature 279 (1979) 381-384
51. Yao X., Liu Y., and Lin G. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3 2 (1999) 82-102