Forecasting Granular Audience Size for Online Advertising
  Orchestration of campaigns for online display advertising requires marketers
to forecast audience size at the granularity of specific attributes of web
traffic, characterized by the categorical nature of all attributes (e.g. {US,
Chrome, Mobile}). With each attribute taking many values, the very large
attribute combination set makes estimating audience size for any specific
attribute combination challenging. We modify Eclat, a frequent itemset mining
(FIM) algorithm, to accommodate categorical variables. For consequent frequent
and infrequent itemsets, we then provide forecasts using time series analysis
with conditional probabilities to aid approximation. An extensive simulation,
based on typical characteristics of audience data, is built to stress test our
modified-FIM approach. In two real datasets, comparison with baselines
including neural network models, shows that our method lowers computation time
of FIM for categorical data. On hold out samples we show that the proposed
forecasting method outperforms these baselines.
