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Time Series Modeling of Market Price in Real-Time Bidding
Du, Manxing; Hammerschmidt, Christian; Varisteas, Georgios et al.
2019In 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Peer reviewed
 

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Keywords :
Time Series; Real-Time Bidding; Recurrent Neural Network
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Du, Manxing ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Hammerschmidt, Christian ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Varisteas, Georgios ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
State, Radu  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Brorsson, Mats Hakan  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Zhang, Zhu;  Iowa State University
External co-authors :
yes
Language :
English
Title :
Time Series Modeling of Market Price in Real-Time Bidding
Publication date :
April 2019
Event name :
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Event place :
Bruges, Belgium
Event date :
April 24-26
Audience :
International
Main work title :
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Peer reviewed :
Peer reviewed
FnR Project :
FNR11277622 - Self-learning Predictive Algorithms: From Design To Scalable Implementation, 2016 (01/03/2016-31/10/2019) - Manxing Du
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