Abstract Today's Internet marketing ecosystems are very complex, with many competing players, transactions concluded within milliseconds, and hundreds of different parameters to be analyzed in the decision-making process. In addition, both sellers and buyers operate under uncertainty, without full information about auction results, purchasing preferences, and strategies of their competitors or suppliers. As a result, most market participants strive to optimize their trading strategies using advanced machine learning algorithms. In this publication, we propose a new approach to determining reserve-price strategies for publishers, focusing not only on the profits from individual ad impressions, but also on maximum coverage of advertising space. This strategy combines the heuristics developed by experienced RTB consultants with machine learning forecasting algorithms like ARIMA, SARIMA, Exponential Smoothing, and Facebook Prophet. The paper analyses the effectiveness of these algorithms, recommends the best one, and presents its implementation in real environment. As such, its results may form a basis for a competitive advantage for publishers on very demanding online advertising markets.

中文翻译：

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基于时间序列预测的实时在线竞价市场发布者的底价优化

摘要 当今的互联网营销生态系统非常复杂，竞争者众多，交易在几毫秒内完成，在决策过程中需要分析数百个不同的参数。此外，买卖双方都在不确定的情况下运作，没有关于拍卖结果、购买偏好以及竞争对手或供应商​​策略的完整信息。因此，大多数市场参与者都在努力使用先进的机器学习算法来优化他们的交易策略。在本出版物中，我们提出了一种确定发布商保留价格策略的新方法，不仅关注单个广告展示的利润，还关注广告空间的最大覆盖率。该策略将经验丰富的 RTB 顾问开发的启发式方法与 ARIMA、SARIMA、指数平滑和 Facebook Prophet 等机器学习预测算法相结合。论文分析了这些算法的有效性，推荐了最佳算法，并展示了其在实际环境中的实现。因此，它的结果可能会为出版商在非常苛刻的在线广告市场上的竞争优势奠定基础。