Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart of strategic management, yet not sufficiently motivated by data analytics driven controlling. One of the main obstacles in using e.g. count data time series for M&A seems to be the fact that the intensity of M&A is time varying at least in certain business sectors, e.g. communications. We propose a new automatic procedure to bridge this obstacle using novel statistical methods. The proposed approach allows for a selection of adaptive windows in count data sets by detecting significant changes in the intensity of events. We test the efficacy of the proposed method on a simulated count data set and put it into action on various M&A data sets. It is robust to aberrant behaviour and generates accurate forecasts for the evaluated business sectors. It also provides guidance for an a-priori selection of fixed windows for forecasting. Furthermore, it can be generalized to other business lines, e.g. for managing supply chains, sales forecasts, or call center arrivals, thus giving managers new ways for incorporating statistical modeling in strategic planning decisions.

翻译：公司环境中的战略规划往往以经验和直觉为基础,尽管内部数据通常可以提供,而且可以成为宝贵的信息来源。预测合并和收购(并购)事件是战略管理的核心,但却没有受到数据分析驱动的控制的充分动力。在使用合并和收购数据时间序列方面的主要障碍之一似乎是,合并和收购的强度至少在某些商业部门(例如通信部门)存在时间差异。我们建议采用新的自动程序,利用新的统计方法来弥合这一障碍。拟议办法允许通过发现事件强度的重大变化,在数组数据集中选择适应性窗口。我们测试模拟计数数据集的拟议方法的功效,并将其纳入各种合并和收购数据集的行动。它对于异常行为是强有力的,为经过评价的商业部门提出准确的预测。它还为优先选择用于预测的固定窗口提供指导。此外,它可以推广到其他商业领域,例如管理供应链、销售预测或呼叫中心抵达者,从而给管理人员提供新的战略决策模式。