Forecasting model for bidding behavior of advertisers based on HMM

In order to precisely study the advertisers´ bidding behavior, in this paper, we proposed a HMM(Hidden Markov Model) forecasting model using the historical auction data of advertisers, and predicted the advertisers´ bidding sequences in the future with this model. In the process of establishing HMM model for advertisers´ bidding behavior, we define the bidding as the hidden variable, and define the position that advertiser obtained as the observable variable in this model. In order to verify the effectiveness of this approach, we compared this method with existing Bayesian forecasting model, and found that HMM model predicted advertisers´ bid closer to the actual auction; In addition, we used this method in the TAC/AA(Trading Agent Competition/Ad Auctions) game platform, and finally achieved good results. Therefore, HMM bidding behavior model can well simulate advertisers´ bidding sequence, and provide a very good forecasting method for advertisers´ bidding, and also help search engines to develop appropriate auction mechanism by predicting advertisers´ bidding sequences.