  Real-time Bidding (RTB) advertisers wish to \textit{know in advance} the
expected cost and yield of ad campaigns to avoid trial-and-error expenses.
However, Campaign Performance Forecasting (CPF), a sequence modeling task
involving tens of thousands of ad auctions, poses challenges of evolving user
interest, auction representation, and long context, making coarse-grained and
static-modeling methods sub-optimal. We propose \textit{AdVance}, a time-aware
framework that integrates local auction-level and global campaign-level
modeling. User preference and fatigue are disentangled using a time-positioned
sequence of clicked items and a concise vector of all displayed items.
Cross-attention, conditioned on the fatigue vector, captures the dynamics of
user interest toward each candidate ad. Bidders compete with each other,
presenting a complete graph similar to the self-attention mechanism. Hence, we
employ a Transformer Encoder to compress each auction into embedding by solving
auxiliary tasks. These sequential embeddings are then summarized by a
conditional state space model (SSM) to comprehend long-range dependencies while
maintaining global linear complexity. Considering the irregular time intervals
between auctions, we make SSM's parameters dependent on the current auction
embedding and the time interval. We further condition SSM's global predictions
on the accumulation of local results. Extensive evaluations and ablation
studies demonstrate its superiority over state-of-the-art methods. AdVance has
been deployed on the Tencent Advertising platform, and A/B tests show a
remarkable 4.5\% uplift in Average Revenue per User (ARPU).
