| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === 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(DSP). 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 realtime
due to the high volume of features. Therefore, we propose a method predicting traffic of
requests of advertisements from perspective of DSPs. The features we used are more simple and
easy to be extracted from history 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.
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