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Fuzzy Time Series Prediction Model

  • Conference paper
Information Intelligence, Systems, Technology and Management (ICISTM 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 141))

Abstract

The main objective to design this proposed model is to overcome the drawbacks of the exiting approaches and derive more robust & accurate methodology to forecast data. This innovative soft computing time series model is designed by joint consideration of three key points (1) Event discretization of time series data (2 Frequency density based partitioning (3) Optimizing fuzzy relationship in inventive way. As with most of cited papers, historical enrollment of university of Alabama is used in this study to illustrate the new forecasting process. Subsequently, the performance of the proposed model is demonstrated by making comparison with some of the pre-existing forecasting methods. In general, the findings of the study are interesting and superior in terms of least Average Forecasting Error Rate (AFER) and Mean Square Error (MSE) values.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Bindu Garg, M. M. Sufyan Beg & B. M. Imran

  2. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    A. Q. Ansari

Authors
  1. Bindu Garg
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  2. M. M. Sufyan Beg
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  3. A. Q. Ansari
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  4. B. M. Imran
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Editor information

Editors and Affiliations

  1. Computer Science, College of Engineering and Science, Louisiana Tech University, 71272, Ruston, LA, USA

    Sumeet Dua

  2. CISE Department, CSE 301, University of Florida, 32611, Gainesville, FL, USA

    Sartaj Sahni

  3. Management Development Institute, 122 007, Sukhrali, Gurgaon, India

    D. P. Goyal

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© 2011 Springer-Verlag Berlin Heidelberg

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Garg, B., Sufyan Beg, M.M., Ansari, A.Q., Imran, B.M. (2011). Fuzzy Time Series Prediction Model. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_14

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  • DOI: https://doi.org/10.1007/978-3-642-19423-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19422-1

  • Online ISBN: 978-3-642-19423-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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