YANG Xianglin, TONG Yunhai, YANG Jianjun, YAN Hong, WANG Hongbo, TAN Shaohua. Improved IAMB with Expanded Markov Blanket for High-Dimensional Time Series Prediction[J]. Chinese Journal of Electronics, 2016, 25(2): 264-269. doi: 10.1049/CJE.2016.03.011
Citation: YANG Xianglin, TONG Yunhai, YANG Jianjun, YAN Hong, WANG Hongbo, TAN Shaohua. Improved IAMB with Expanded Markov Blanket for High-Dimensional Time Series Prediction[J]. Chinese Journal of Electronics, 2016, 25(2): 264-269. doi: 10.1049/CJE.2016.03.011

Improved IAMB with Expanded Markov Blanket for High-Dimensional Time Series Prediction

doi: 10.1049/CJE.2016.03.011
Funds:  This work is supported by State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center (No.SMFA12B09).
  • Received Date: 2014-03-20
  • Rev Recd Date: 2014-06-11
  • Publish Date: 2016-03-10
  • The classic regression model for multivariate time series prediction suffers from the curse of dimensionality because the least squares estimates become unreliable when the predictors of the time series are large and the number of samples is limited. The classic methods such as ridge regression, lasso regression, principal component regression are researched to solve the problem above. The ridge regression and principal component regression can not give the clear interpretation features for prediction, and lasso regression does not work well under collinearity, also the selections given by lasso regression are unstable and very sensitive to minor perturbations of the data. A practical method based on improved Incremental association Markov blanket (IAMB) with Expanded Markov blanket (EMB) was proposed for high-dimensional time series prediction. The EMB was constructed with simultaneous predictors and past predictors for time series. Since the faithfulness condition and reliable conditional independence test are not satisfied for high-dimensional time series with limited samples in practical applications. The symmetry of Markov blanket (MB) and the partial correlation coefficient criterion for conditional independent test were employed to learn the EMB, on which the regression was used for prediction. Empirical results show that our method based on EMB for macroeconomic prediction has less mean-square forecast error than other classic methods, especially when predicting the value with sharp fluctuation.
  • loading
  • T. Buchen and K. Wohlrabe, "Forecasting with many predictors: Is boosting a viable alternative?", Economics Letters, Vol.113, No.1, pp.16-18, 2011.
    R. Giacomini and H. White, "Tests of conditional predictive ability", Econometrica, Vol.74, No.6, pp.1545-1578, 2006.
    J.H. Stock and M.W. Watson, "Forecasting using principal components from a large number of predictors", Journal of the American Statistical Association, Vol.97, No.460, pp.1167-1179, 2002.
    C.D. Mol, D. Giannone and L. Reichlin, "Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components", Journal of Econometrics, Vol.146, No.2, pp.318-328, 2008.
    J.H. Stock and M.W. Watson, "Macroeconomic forecasting using diffusion indexes", Journal of Business and Economic Statistics, Vol.20, No.2, pp.147-162, 2002.
    C. Fernandez, E. Ley and M.F.J. Steel, "Benchmark priors for Bayesian model averaging", Journal of Econometrics, Vol.100, No.2, pp.381-427, 2001.
    C.F. Aliferis, A. Statnikov, I. Tsamardinos, et al., "Local causal and Markov blanket induction for causal discovery and feature selection for classification part i: Algorithms and empirical evaluation", J. Machine Learn. Res., No.11, pp.171-234, 2010.
    P. Spirtes, C.N. Glymour and R. Scheines, Causation, Prediction, and Search, Vol.2, MIT Press, Cambridge, Mass, 2000.
    Y. Zhang, Z. Zhang, K. Liu, et al., "An improved IAMB algorithm for Markov blanket discovery", Journal of Computers, Vol.5, No.11, pp.1755-1761, 2000.
    I. Tsamardinos, C.F Aliferis and A. Statnikovs, "Algorithms for large scale Markov blanket discovery", FLAIRS Conference, Florida, USA, pp.376-381, 2003.
    S.K. Fu and M.C. Desmarais, "Markov blanket based feature selection: A review of past decade", ICDMKE, London, UK, pp.321-328, 2010.
    D. Koller and M. Sahami, "Toward optimal feature selection", ICML, Bari, Italy, pp.284-292, 1996.
    D. Margaritis and S. Thrun, "Bayesian network induction via local neighborhoods", NIPS, Denver, Colorado, USA, pp.505-511, 1999.
    J.P. Pellet and A. Elisseeff, "Using Markov blankets for causal structure learning", J. Machine Learn. Res., No.9, pp.1295-1342, 2008.
    Z. Yishi, X. Hong, H. Yang, et al., "S-IAMB algorithm for Markov blanket discovery", APCIP 2009, IEEE, Shenzhen, Guangdong, China, Vol.2, pp.379-382, 2009.
    Y. Zhang, Z. Zhang, K. Liu, et al., "An improved IAMB algorithm for Markov blanket discovery", Journal of Computers, Vol.5, No.11, pp.1755-1761, 2010.
    S. Yaramakala and D. Margaritis, "Speculative Markov blanket discovery for optimal feature selection", Proceedings of the Fifth ICDM, Washington, DC, USA, pp.809-812, 2005.
    I. Tsamardinos, L.E. Brown and C.F. Aliferis, "The max-min hillclimbing Bayesian network structure learning algorithm", Machine Learning, Vol.65, No.1, pp.31-78, 2006.
    C. Aliferis, I. Tsamardinos and A. Statnikov. "HITON, a novel Markov blanket algorithm for optimal variable selection", AMIA Annual Symposium Proceedings, pp.21-25, 2003.
    J.M. Pena, R. Nilsson, J. Bjorkegren, et al., "Towards scalable and data efficient learning of Markov boundaries", International Journal of Approximate Reasoning, Vol.45, No.2, pp.211-231, 2007.
    S.K. Fu and M.C. Desmarais, "Local learning algorithm for Markov blanket discovery", Advances in AI, Gold Coast, Australia, pp.68-79, 2007.
    S.K. Fu and M.C. Desmarais, "Fast Markov blanket discovery algorithm via local learning within single pass", Advances in AI, Windsor, Canada, pp.96-107, 2008.
    Z.X. Wang and L.W. Chan, "Learning Bayesian network from Markov random fields: An efficient algorithm for linear models", ACM Transaction on Knowledge Discovery from Data, Vol.6, No.3, Article No.10, 2012.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (97) PDF downloads(654) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return