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Enhancing Forecasting Performance of Multivariate Time Series Using New Hybrid Feature Selection

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Emerging Intelligent Computing Technology and Applications (ICIC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 304))

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Abstract

The aim of this study is to propose a new hybrid feature selection model to improve the performance of multivariate time series (MTS) forecasting under uncertainty situation. This new hybrid model is called cooperative feature selection (CFS) and consists of two different component; GRA Analyzer and ANN Optimizer. The performance of CFS is evaluated on KLSE close price. The statistical analysis of the results shows that CFS has high ability to recognize and remove irrelevant input for obtaining optimum input factors, shortening the learning time and improving forecasting accuracy for vague MTS.

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References

  1. Crone, S.F., Kourentzes, N.: Feature Selection for Time Series Prediction. A Combine Filter & Wrapper Approach for Neural Network. Neurocomputing 73, 1923–1936 (2010)

    Article  Google Scholar 

  2. Kabir, M.M., Islam, M.M., Murase, K.: A New Wrapper Feature Selection Ap-proach Using Neural Network. Neurocomputing 73, 3273–3283 (2010)

    Article  Google Scholar 

  3. Bu, H., Xia, J.: Hybrid Feature Selection Mechanism Based High Di-mensional Datasets Reduction. Energy Procedia 11, 4973–4978 (2011)

    Article  Google Scholar 

  4. Mehdi, K., Seyed, H.R., Mehdi, B.: A New Hybrid ANN and Fuzzy Regression Model for TS forecasting. Fuzzy Sets & Sys. 159, 769–786 (2007)

    Google Scholar 

  5. Lin, Y.H., Lee, P.C., Chang, T.P.: Practical Expert Diagnosis Model Based on Grey Relational Analysis Technique. Expert Systems with Applications 36, 1523–1528 (2009)

    Article  Google Scholar 

  6. Ip, W.C., Hu, B.Q., Wong, H., Xia, J.: Application of Grey Relational Method to River Environment Quality Evaluation in China. Journal of Hydrology 379, 284–290 (2009)

    Article  Google Scholar 

  7. Verikas, A., Bacauskiene, M.: Feature Selection with ANN. Pattern Recognition Letters 23, 1323–1335 (2002)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Soft Computing Research Group, Faculty of Science Computer and Information System, Universiti Teknologi Malaysia, 83010, Skudai, Johor, Malaysia

    Roselina Sallehuddin, Siti Mariyam Shamsuddin & Noorfa Haszlinna Mustafa

Authors
  1. Roselina Sallehuddin
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  2. Siti Mariyam Shamsuddin
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  3. Noorfa Haszlinna Mustafa
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Sallehuddin, R., Shamsuddin, S.M., Mustafa, N.H. (2012). Enhancing Forecasting Performance of Multivariate Time Series Using New Hybrid Feature Selection. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_54

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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