Information Sciences Institute, University of Southern California, Marina del Rey, CA, 90292, USA.
Department of Computer Science, University of Chile, Santiago, Chile.
Sci Rep. 2020 Nov 24;10(1):20427. doi: 10.1038/s41598-020-77091-1.
Applications from finance to epidemiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only partially observed. We demonstrate that a system's predictability degrades as a function of temporal sampling, regardless of the adopted forecasting model. We quantify the loss of predictability due to sampling, and show that it cannot be recovered by using external signals. We validate the generality of our theoretical findings in real-world partially observed systems representing infectious disease outbreaks, online discussions, and software development projects. On a variety of prediction tasks-forecasting new infections, the popularity of topics in online discussions, or interest in cryptocurrency projects-predictability irrecoverably decays as a function of sampling, unveiling predictability limits in partially observed systems.
从金融到流行病学和网络安全，各种应用都需要对动态现象进行准确预测，但这些现象往往只能部分观测到。我们证明，无论采用何种预测模型，系统的可预测性都会随时间采样而降低。我们量化了由于采样而导致的可预测性损失，并表明无法通过使用外部信号来恢复。我们在代表传染病爆发、在线讨论和软件开发项目的真实部分观测系统中验证了我们理论发现的普遍性。在各种预测任务中——预测新的感染、在线讨论中主题的流行度或对加密货币项目的兴趣——可预测性都会随采样而不可恢复地衰减，揭示了部分观测系统中的可预测性限制。
