A typical problem for a search engine (hosting sponsored search service) is to provide the advertisers with a forecast of the number of impressions his/her ad is likely to obtain for a given bid. Accurate forecasts have high business value, since they enable advertisers to select bids that lead to better returns on their investment. They also play an important role in services such as automatic campaign optimization. Despite its importance the problem has remained relatively unexplored in literature. Existing methods typically overfit to the training data, leading to inconsistent performance. Furthermore, some of the existing methods cannot provide predictions for new ads, i.e., for ads that are not present in the logs. In this paper, we develop a generative model based approach that addresses these drawbacks. We design a Bayes net to capture inter-dependencies between the query traffic features and the competitors in an auction. Furthermore, we account for variability in the volume of query traffic by using a dynamic linear model. Finally, we implement our approach on a production grade MapReduce framework and conduct extensive large scale experiments on substantial volumes of sponsored search data from Bing. Our experimental results demonstrate significant advantages over existing methods as measured using several accuracy/error criteria, improved ability to provide estimates for new ads and more consistent performance with smaller variance in accuracies. Our method can also be adapted to several other related forecasting problems such as predicting average position of ads or the number of clicks under budget constraints.