  Online advertising revenues account for an increasing share of publishers'
revenue streams, especially for small and medium-sized publishers who depend on
the advertisement networks of tech companies such as Google and Facebook. Thus
publishers may benefit significantly from accurate online advertising revenue
forecasts to better manage their website monetization strategies. However,
publishers who only have access to their own revenue data lack a holistic view
of the total ad market of publishers, which in turn limits their ability to
generate insights into their own future online advertising revenues. To address
this business issue, we leverage a proprietary database encompassing Google
Adsense revenues from a large collection of publishers in diverse areas. We
adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based
architecture to predict publishers' advertising revenues. We leverage multiple
covariates, including not only the publisher's own characteristics but also
other publishers' advertising revenues. Our prediction results outperform
several benchmark deep-learning time-series forecast models over multiple time
horizons. Moreover, we interpret the results by analyzing variable importance
weights to identify significant features and self-attention weights to reveal
persistent temporal patterns.
