École de technologie supérieure - ÉTS Montréal
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Forecasting the time series of eCPMs in online advertising
Online advertising has become the main channel of revenue for many web publishers. With the development of real-time bidding (RTB), publishers now are able to sell their advertising space in real-time, where the price is determined by the demand of the market at a time. In this thesis, we made an attempt to help publishers forecast the expected effective cost per mille (eCPM) of their ads in an RTB market in the next 30 days using the historical data of eCPM in the past 2 years. First, we explore the use of an Auto Regressive Integrated Moving Average (ARIMA) model to fit the time series of historical eCPM and make the forecast. Second, we examine the distribution of eCPM over a period and develop a confidence indicator which suggests the market volatility. The training and forecasting process is then integrated into our industrial partner data pipeline for evaluation in a production environment.