Predictive modeling and time-pattern analysis are increasingly critical in this swiftly shifting retail environment to improve operational efficiency and informed decision-making. This paper reports a comprehensive application of state-of-the-art machine learning to the retailing domain with a specific focus on association rule mining, sequential pattern mining, and time-series forecasting. Association rules: Relationship Mining This provides the key product relationships and customer buying patterns that form the basis of individually tailored marketing campaigns. Sequential pattern mining: Using the PrefixSpan algorithm, it identifies frequent sequences of purchasing products-extremely powerful insights into consumer behavior and also better management of the inventories. What is applied for sales trend forecasting models Prophet applies on historical transaction data over seasonality, holidays, and long-term growth. The forecast results allow predicting demand variations, thus helping in proper inventory alignment and avoiding overstocking or understocking of inventory. Our results are checked through the help of metrics like MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) to ensure our predictions are strong and accurate. We will combine the aspects of all of these techniques to prove how predictive modeling and temporal pattern analysis can help optimize control over inventory, enhance marketing effectiveness, and position retail businesses as they rise to ever greater heights. This entire methodology demonstrates the flexibility with which data-driven strategies can be leveraged to revitalize traditional retailing practices.

翻译：在快速变化的零售环境中，预测建模与时间模式分析对于提升运营效率与辅助决策制定日益关键。本文系统报告了前沿机器学习技术在零售领域的综合应用，重点关注关联规则挖掘、序列模式挖掘与时间序列预测三大方向。关联规则挖掘揭示了关键产品关联与顾客购买模式，为个性化营销活动奠定基础。序列模式挖掘采用PrefixSpan算法识别高频购买商品序列，为消费者行为分析提供深度洞察，同时优化库存管理。针对销售趋势预测，我们应用Prophet模型对历史交易数据进行建模，综合考虑季节性、节假日及长期增长趋势。预测结果可有效预判需求波动，从而实现精准库存调配，避免积压或缺货风险。我们通过平均绝对误差（MAE）与均方根误差（RMSE）等指标验证模型，确保预测结果的稳健性与准确性。通过整合上述技术，本研究论证了预测建模与时间模式分析如何协同优化库存控制、提升营销效能，助力零售企业在竞争中获得持续优势。整套方法论展现了数据驱动策略改造传统零售实践的可扩展性与实践价值。