Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach

TL;DR

This paper introduces an interpretable deep learning approach using the Temporal Fusion Transformer to forecast online advertising revenues, incorporating multiple data sources for improved accuracy and insights.

Contribution

It presents a novel application of TFT with multi-source data for revenue prediction and provides interpretability through feature importance and attention analysis.

Findings

Outperforms benchmark models in revenue forecasting accuracy.

Utilizes proprietary multi-publisher data for holistic market insights.

Provides interpretability of model decisions through attention mechanisms.

Abstract

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.