6,631
Views
40
CrossRef citations to date
0
Altmetric
Research Papers

Practical Bayesian support vector regression for financial time series prediction and market condition change detection

ORCID Icon & ORCID Icon
Pages 1403-1416 | Received 08 Apr 2016, Accepted 18 Nov 2016, Published online: 09 Mar 2017

References

  • Agrawal, S. and Goyal, N., Thompson sampling for contextual Bandits with linear payoffs. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, 2013, 127–135.
  • Bergstra, J. and Bengio, Y., Random search for hyper-parameter optimization. J. Mach. Learn. Res., 2012, 13(1), 281–305.
  • Brochu, E., Cora, V. and de Freitas, N., A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. 2010, preprint, arXiv:1012.2599.
  • Cao, L.J. and Tay, F.E., Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Networks, 2003, 14(6), 1506–1518.
  • Chang, C. and Lin, C., LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011, 2(3), 1–27.
  • Chapelle, O. and Li, L., An empirical evaluation of thompson sampling. Adv. Neural Inf. Proc. Syst 2011, 24, 2249–2257.
  • Desautels, T., Krause, A. and Burdick, J., Parallelizing exploration-exploitation tradeoffs with Gaussian process bandit optimization. J. Mach. Learn. Res., 2014, 15(1), 3873–3923.
  • Dorard, L., Glowacka, D. and Shawe-Taylor, J., Gaussian process modelling of dependencies in multi-armed bandit problems. Proceedings of the 10th International Symposium on Operations Research, Nova Gorica, Slovenia, 2009, 77–84.
  • Gao, J., Gunn, S., Harris, C. and Brown, M., A probabilistic framework for SVM regression and error bar estimation. Mach. Learn., 2002, 46(1), 71–89.
  • Ginsbourger, D. and Riche, R., Dealing with asynchronicity in parallel Gaussian process based global optimization. 4th International Conference of the ERCIM WG on Computing & Statistics (ERCIM’11), London, UK, 2011.
  • Gündüz, Y. and Uhrig-Homburg, M., Predicting credit default swap prices with financial and pure data-driven appraoches. Quant. Finance, 2011, 11(12), 1709–1727.
  • Kaufmann, E., Korda, N. and Munos, R., Thompson sampling: An asymptotically optimal finite-time analysis. Algorithmic Learn. Theory 2012, 7568, 199–213.
  • Kim, K., Financial time series forecasting using support vector machines. Neurocomputing, 2003, 55(1), 307–319.
  • Lu, C., Lee, T. and Chiu, C., Financial time series forecasting using independent component analysis and support vector regression. Decis. Support Syst., 2009, 47(2), 115–125.
  • MacKay, D., Bayesian interpolation. Neural Comput., 1992, 4(3), 415–447.
  • Martinez-Cantin, R., BayesOpt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits. J. Mach. Learn. Res., 2014, 15(1), 3735–3739.
  • Mohri, M. and Rostamizadeh, A., Stability bounds for non-iid processes. Adv. Neural Inf. Process. Syst., 2008, 20, 1025–1032.
  • Müller, K., Smola, A., Rätsch, G., Sch{\"o}lkopf, B., Kohlmorgen, J., Vapnik, V. Predicting time series with support vector machines. In Artificial Neural Networks: ICANN’97, Vol. 20, edited by W. Gerstner , A. Germond , M. Hasler and J.D. Nicoud, pp. 999–1004, 1997 (Springer: Berlin).
  • Ralaivola, L., Szafranski, M. and Stempfel, G., Chromatic PAC-Bayes bounds for non-iid data: Applications to ranking and stationary-mixing processes. J. Mach. Learn. Res. 2010, 11, 1927–1956.
  • Russo, D. and Van Roy, B., Learning to optimize via posterior sampling. Math. Oper. Res., 2014, 39(4), 1221–1243.
  • Scott, S., A modern Bayesian look at the multi-armed bandit. Appl. Stochastic Models Bus Ind., 2010, 26(6), 639–658.
  • Shahriari, B., Swersky, K., Wang, Z., Adams, R. and de Freitas, N., Taking the human out of the loop: A review of bayesian optimization. Proc. IEEE, 2016, 104(1), 148–175.
  • Smola, A. and Schölkopf, B., A tutorial on support vector regression. Statistics Comput., 2004, 14(3), 199–222.
  • Snoek, J., Larochelle, H. and Adams, R., Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst., 2012, 2951–2959.
  • Srinivas, N., Krause, A., Kakade, S. and Seeger, M., Gaussian process optimization in the bandit setting: No regret and experimental design. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 2010, 1015–1022.
  • Tay, F.E. and Cao, L., Application of support vector machines in financial time series forecasting. Omega, 2001, 29(4), 309–317.