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Predicting Online Auction Final Prices Using Time Series Splitting and Clustering

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7235)

Abstract

Online auctions allows users to sell and buy a variety of goods, and they are now one of the most important web services. Predicting final prices on online auctions is a hard task. However, there has been much pioneering work over the past ten years. In this paper, we propose a novel method for predicting final prices using a time series splitting and clustering method to provide higher accuracy. An evaluation of the effectiveness of our method is also described in the paper.

Keywords

  • Time series
  • Prediction
  • Online auction
  • Clustering

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© 2012 Springer-Verlag Berlin Heidelberg

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Yokotani, T., Huang, HH., Kawagoe, K. (2012). Predicting Online Auction Final Prices Using Time Series Splitting and Clustering. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_18

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  • DOI: https://doi.org/10.1007/978-3-642-29253-8_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29252-1

  • Online ISBN: 978-3-642-29253-8

  • eBook Packages: Computer ScienceComputer Science (R0)