Predicting traffic of online advertising in real-time bidding systems from perspective of demand-side platforms

Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, i...

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Bibliographic Details
Published in2016 IEEE International Conference on Big Data (Big Data) pp. 3491 - 3498
Main Authors Hsu-Chao Lai, Wen-Yueh Shih, Jiun-Long Huang, Yi-Cheng Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Advertising
Demand Side Platform
Digital signal processing
Feature extraction
Online Advertisement
Predictive models
Real Time Bidding
Real-time systems
Request Traffic
Time series analysis
Training
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Summary:Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.
DOI:10.1109/BigData.2016.7841012