This paper intends to contribute to the field of trend forecasting by proposing a new forecasting approach for stock market prices and geopolitical time series data of economic, financial and geopolitical importance. Designing models which account for every possible exogenous variable of relevance to a time series in question can often be an onerous and impractical task. Instead, this paper explores a new method which uses periods of decreased significance in the variable of foremost importance as a window of opportunity to observe the possible effects other variables may be having in a general way for the purpose of trend forecasting. When the latter variables are too unquantifiable to be accounted for in a model, having the ability to nonetheless discern their overall influence can be useful for anticipating trend changes. The proposed method was used in conjunction with the existing method of exponential smoothing to generate forecasts. It was also applied alone and contrasted with the results of exponential smoothing when used separately. This paper specifically addresses the ability of the newly proposed method to forecast the upwards/downwards extrapolation of the weekly trend for 9 weeks on stock closing prices for five companies of interest (Apple Inc, Amazon.com Inc, General Electric Company, Intel Corporation, and Alcoa Corporation). It was also applied to forecasting the annual trend for 9 years of Afghan asylum seeker data. These differing areas were chosen in order to demonstrate applications in finance as well as international relations. The empirical results and 95% confidence intervals indicate a clear advantage when the newly proposed method is used both in conjunction with exponential smoothing and on its own.

中文翻译：

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金融和地缘政治数据趋势预测的一种方法：推断未知外生变量的影响

本文旨在通过为股票市场价格和具有经济，金融和地缘政治重要性的地缘政治时间序列数据提出一种新的预测方法，为趋势预测领域做出贡献。设计模型以说明与所讨论的时间序列相关的每个可能的外生变量通常是一项繁重而不切实际的任务。取而代之的是，本文探索了一种新方法，该方法使用最重要的变量的显着性降低的时间段作为机会窗口，以观察其他变量可能以一般方式对趋势预测产生的可能影响。当后面的变量无法量化而无法在模型中说明时，仍然能够分辨出它们的整体影响力对于预测趋势变化很有用。将该方法与现有的指数平滑方法结合使用以生成预测。它也可以单独使用，并与单独使用时的指数平滑结果形成对比。本文专门探讨了新提出的方法能够预测五家相关公司（苹果公司，亚马逊公司，通用电气公司，英特尔公司，和美国铝业公司（Alcoa Corporation）。它还被用于预测阿富汗寻求庇护者数据的9年年度趋势。选择这些不同的领域是为了展示在金融以及国际关系中的应用。