OSINT时间序列预测方法分析

Dmytro Lande, Anatolii Feher
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引用次数: 0

摘要

时间序列预测在现代决策和战术选择过程中是一个重要的领域,在OSINT技术的背景下,这种方法可以帮助预测事件并允许对事件进行有效的响应。为此,选择LSTM、ARIMA、LPPL (JLS)、N-gram作为时间序列预测方法,并基于利用OSINT技术获得并生成的北约、himars、starlink和网络威胁预警的定量提及时间序列实现其简单形式。在此基础上,研究了它们的整体有效性以及与OSINT技术结合使用形成未来预测的可能性。
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OSINT Time Series Forecasting Methods Analysis
Time series forecasting is an important niche in the modern decision-making and tactics selection process, and in the context of OSINT technology, this approach can help predict events and allow for an effective response to them. For this purpose, LSTM, ARIMA, LPPL (JLS), N-gram were selected as time series forecasting methods, and their simple forms were implemented based on the time series of quantitative mentions of nato, himars, starlink and cyber threats statings obtained and generated using OSINT technology. Based on this, their overall effectiveness and the possibility of using them in combination with OSINT technology to form a forecast of the future were investigated.
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