This paper presents Delfos, a model for multi-temporal analysis based on historical contexts. The article describes the model architecture, implementation, and tests with two real datasets to evaluate the functionality and its potential to real applications. The first scenario used historical data from an electric power transmission substation, and the second with data from the performance analysis of futsal athletes. The results obtained through inference rules perform with 100% accuracy of the Profiles creation and that these are a better representation of the present, than only the current context data of the application. The results on the prediction of contexts were relevant in the case of the existence of behavior patterns in the contexts of the analyzed entities. For the application related to the electric power transmission substation, the assertiveness was between 86% and 92% in a 12-hour interval. In the application of performance analysis of futsal athletes, we made predictions about the athletes movement, with results varying between 72% and 92% for the time of up to 20 seconds. The results were encouraging and show potential for implementing Delfos in real-life situations.

中文翻译：

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DELFOS：基于情境历史的多时相分析模型


本文提出了 Delfos，一种基于历史背景的多时间分析模型。本文描述了模型架构、实现以及使用两个真实数据集进行的测试，以评估其功能及其对实际应用程序的潜力。第一个场景使用来自输电变电站的历史数据，第二个场景使用来自五人制足球运动员表现分析的数据。通过推理规则获得的结果在配置文件创建方面具有 100% 的准确性，并且这些结果比仅应用程序的当前上下文数据更好地表示了当前情况。上下文预测的结果与所分析实体的上下文中存在行为模式的情况相关。对于与输电变电站相关的应用，12 小时间隔内的自信度在 86% 到 92% 之间。在五人制足球运动员表现分析的应用中，我们对运动员的动作进行了预测，结果在长达 20 秒的时间里变化在 72% 到 92% 之间。结果令人鼓舞，并显示了在现实生活中实施 Delfos 的潜力。