Reference : Time Series Modeling of Market Price in Real-Time Bidding
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/39730
Time Series Modeling of Market Price in Real-Time Bidding
English
Du, Manxing mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Hammerschmidt, Christian mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Varisteas, Georgios mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Brorsson, Mats Hakan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Zhang, Zhu mailto [Iowa State University]
Apr-2019
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Yes
International
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
April 24-26
Bruges
Belgium
[en] Time Series ; Real-Time Bidding ; Recurrent Neural Network
[en] Real-Time-Bidding (RTB) is one of the most popular online
advertisement selling mechanisms. Modeling the highly dynamic bidding
environment is crucial for making good bids. Market prices of auctions
fluctuate heavily within short time spans. State-of-the-art methods neglect
the temporal dependencies of bidders’ behaviors. In this paper, the bid
requests are aggregated by time and the mean market price per aggregated
segment is modeled as a time series. We show that the Long Short Term
Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies
in the market price. We further improve the predicting performance by
adding a summary of exogenous features from bid requests.
http://hdl.handle.net/10993/39730
FnR ; FNR11277622 > Manxing Du > > Self-learning predictive algorithms: from design to scalable implementation > 01/03/2016 > 31/10/2019 > 2016

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