Seasonality-Aware Predictive Computing for Generative AI: Anticipating Periodic Computational Demand Through Calendar-Based and Temporal Forecasting

The computational demand of generative artificial intelligence (AI) services is influenced not only by emerging information trends and unpredictable external events but also by recurring temporal patterns associated with holidays, examination periods, shopping seasons, academic calendars, annual events, and other periodic activities. These recurring patterns can generate predictable changes in the volume, semantic composition, and computational characteristics of user requests. However, conventional generative AI inference systems predominantly allocate computational resources in response to requests after they arrive. Although existing predictive caching and workload forecasting approaches investigate future demand, the systematic integration of seasonal demand forecasting with proactive computational preparation remains an important research direction. This paper introduces Seasonality-Aware Predictive Computing for Generative AI (SA-PCGA), a conceptual framework that extends Predictive Computing for Generative AI (PCGA) by incorporating seasonal, calendar-based, and recurring temporal signals into future computational demand prediction. The proposed framework investigates whether generative AI systems can anticipate periodic changes in user information requirements and prepare selected responses, computational states, and inference resources before corresponding requests materialize. It considers multiple sources of seasonal information, including public holidays, examination schedules, school and university calendars, shopping events, annual commercial activities, recurring cultural events, and historical request distributions. These signals are transformed into probabilistic estimates of future request frequency, semantic demand composition, computational expenditure, and expected preparation opportunities. The proposed architecture integrates seasonal signal acquisition, temporal pattern identification, seasonal demand forecasting, future question generation, computational workload estimation, cost-aware precomputation scheduling, reusable resource preparation, and adaptive seasonal updating. A unified optimization formulation evaluates the expected computational benefits of advance preparation against forecasting, generation, validation, storage, maintenance, and prediction-error costs. The framework further distinguishes between stable seasonal patterns, calendar-dependent demand, and irregular event-driven deviations, recognizing that periodic demand is not necessarily stationary or identical across years, regions, and user populations. An experimental methodology is established to evaluate seasonal forecasting accuracy, prediction lead time, computational savings, resource utilization, and response quality under chronological testing conditions. Particular attention is given to comparisons between conventional reactive inference, historical-frequency caching, nonseasonal predictive computing, and seasonality-aware predictive computing. This study does not claim experimentally established performance improvements. Instead, it provides a theoretical and architectural foundation for investigating whether recurring temporal patterns can support more efficient advance preparation in generative AI infrastructure. The proposed direction extends predictive computing from primarily trend-driven anticipation toward calendar-aware and seasonally informed computational planning.