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As advertisers increasingly shift their budgets toward digital advertising,forecasting advertising costs is essential for making budget plans to optimizemarketing campaign returns. In this paper, we perform a comprehensive studyusing a variety of time-series forecasting methods to predict daily averagecost-per-click (CPC) in the online advertising market. We show that forecastingadvertising costs would benefit from multivariate models using covariates fromcompetitors' CPC development identified through time-series clustering. Wefurther interpret the results by analyzing feature importance and temporalattention. Finally, we show that our approach has several advantages overmodels that individual advertisers might build based solely on their collecteddata.

Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape