互联网平台用户流量动态的非混沌模型及其可预测性
Nonchaotic Models and Predictability of the Users' Volume Dynamics on Internet Platforms
摘要：
互联网平台的流量定义了平台的重要特征，如服务价格，广告，运营速度。交通量的估计通常借助于传统的时间序列模型（ARIMA、Holt-Winters等），其在充分去噪信号的短期外推中是成功的。我们提出了一个动力系统的方法建模的基础过程。该方法允许讨论动态的相图和长期趋势的全局定性性质。所提出的模型是非混沌的，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称为{ \it趋势流}。利用新的方法，我们构建了双边平台的用户量模型，可以应用到Amazon.com，Homes.mil或Wikipedia. org。我们考虑了双边平台的模型的推广到多边平台。如果方程是合作的，则流是趋势的，并且它有助于理解平台的属性并可靠地预测长期行为。我们将展示如何从时间序列数据中重建控制微分方程。外部效应被建模为系统的参数（初始条件）。
英文摘要：
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).
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