Reference : Time Series Modeling of Market Price in Real-Time Bidding

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/39730

Title :	Time Series Modeling of Market Price in Real-Time Bidding
Language :	English
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]
Publication date :	Apr-2019
Main document title :	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Peer reviewed :	Yes
Audience :	International
Event name :	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Event date :	April 24-26
Event place (city) :	Bruges
Event country :	Belgium
Keywords :	[en] 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.
Permalink :	http://hdl.handle.net/10993/39730
FnR project :	FnR ; FNR11277622 > Manxing Du > > Self-learning predictive algorithms: from design to scalable implementation > 01/03/2016 > 31/10/2019 > 2016

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Limited access esannV2.pdf Publisher postprint 380.06 kB Request a copy

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map