论文标题
互联网平台上用户音量动态的非偶然模型和可预测性
Nonchaotic Models and Predictability of the Users' Volume Dynamics on Internet Platforms
论文作者
论文摘要
互联网平台的流量定义了平台的重要特征，例如服务价格，广告，运营速度。通常在传统时间序列模型（Arima，Holt-Winters等）的帮助下估算流量，这些模型在短期的推断中成功地推断了足够的信号。我们提出了一种动态系统方法，用于建模基础过程。该方法允许讨论动力学相肖像和长期趋势的全球定性属性。所提出的模型是无聊的，长期预测是可靠的，它解释了各种类型的数字平台的基本属性和趋势。由于这些属性，我们将这些模型的流动称为{\ it趋势流}。利用新方法，我们为用户量构建了双面平台模型，可以应用于Amazon.com，homes.mil或wikipedia.org。我们考虑将双面平台的模型概括为多面平台。如果方程是合作的，则流动趋势，并且有助于理解平台的属性并可靠地预测长期行为。我们展示了如何从时间序列数据中重建管理差分方程。外部效果被建模为系统参数（初始条件）。
Internet platforms' traffic defines important characteristics of platforms, such as price of services, advertisements, speed of operations. The traffic is usually estimated with the help of the traditional time series models (ARIMA, Holt-Winters, etc.), which are successful in short term extrapolations of sufficiently denoised signals. We propose a dynamical system approach for the modeling of the underlying process. The method allows to discuss the global qualitative properties of the dynamics' phase portrait and long term tendencies. The proposed models are nonchaotic, the long term prediction is reliable, and it explains the fundamental properties and trend of various types of digital platforms. Because of these properties, we call the flow of these models the {\it trending flow}. Utilizing the new approach, we construct the two-sided platform models for the volume of users, that can be applied to Amazon.com, Homes.mil or Wikipedia.org. We consider a generalization of the two-sided platforms' models to multi-sided platforms. If the equations' are cooperative, the flow is trending, and it helps to understand the properties of the platforms and reliably predicts the long term behavior. We show how to reconstruct the governing differential equations from time series data. The external effects are modeled as system's parameters (initial conditions).
