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 fundamental predictability limits in partially observed systems.

中文翻译：

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部分观测系统的可预测性极限

从金融到流行病学和网络安全的应用需要对动态现象的准确预测，而这些动态现象通常只能被部分观察到。我们证明，无论采用何种预测模型，系统的可预测性都会随着时间采样而降低。我们量化了由于采样造成的可预测性损失，并表明它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世界部分观察系统中验证了我们的理论发现的普遍性。在各种预测任务中——预测新的感染、在线讨论中话题的流行度或对加密货币项目的兴趣——可预测性作为采样的函数不可恢复地衰减，