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Long-Term Time Series Prediction Using k-NN Based LS-SVM Framework with Multi-Value Integration

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Recent Trends in Information Reuse and Integration

Abstract

Time series modeling and prediction are very attractive topics, which play an important role in many fields such as transportation prediction [4], power prediction [13, 18], and health care study [7]. The purpose of time series prediction is to forecast the values of data points ahead of time, where long-term time series prediction is to make the predictions multi-step ahead. The prediction process is commonly performed by observing and modeling the past values, and assuming that the future values will follow the same trend. When the prediction horizon increases, the uncertainty of the future trend also increases, rendering a more challenging prediction problem. Researchers have dedicated their effort to study how to extract as much knowledge as possible from the past values, and how to better utilize such knowledge for long-term time series prediction. There has been previous research work in order to tackle this challenge based on some classical time series prediction approaches, such as exponential smoothing [12], linear regression [14], autoregressive integrated moving average (ARIMA) [33], support vector machines (SVM) [25], artificial neural networks (ANN) [10, 33], and fuzzy logic [10].

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, 33124, USA

    Zifang Huang & Mei-Ling Shyu

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  1. Zifang Huang
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  2. Mei-Ling Shyu
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Correspondence to Zifang Huang .

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Editors and Affiliations

  1. , Department of Computer Engineering, Tobb University, Sögütözü Caddesi 43, Ankara, 06560, Turkey

    Tansel Özyer

  2. , Department of Electrical Engineering, University of Western Ontario, Building 363, London, N6A 5B9, Ontario, Canada

    Keivan Kianmehr

  3. , Department of Computer Engineering, Tobb University, Söğütözü Caddesi 43, Ankara, 06560, Ankara, Turkey

    Mehmet Tan

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Huang, Z., Shyu, ML. (2012). Long-Term Time Series Prediction Using k-NN Based LS-SVM Framework with Multi-Value Integration. In: Özyer, T., Kianmehr, K., Tan, M. (eds) Recent Trends in Information Reuse and Integration. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0738-6_9

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  • DOI: https://doi.org/10.1007/978-3-7091-0738-6_9

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  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-0737-9

  • Online ISBN: 978-3-7091-0738-6

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