When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
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发表时间：
2021-06
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通讯作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash
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作者：
Harshavardhan Kamarthi;Lingkai Kong;Alexander Rodr'iguez;Chao Zhang;B. Prakash
准确可靠的疫情预测是影响公共卫生规划和疾病缓解的重要问题。现有的大多数流行预测模型忽略了不确定性的量化，导致错误校准的预测。最近在用于不确定性感知时间序列预测的深度神经模型方面的工作也有一些局限性；例如，在贝叶斯神经网络中很难指定有意义的先验，而像深度集成这样的方法在实践中计算代价很高。在本文中，我们填补了这一重要空白。我们将预测任务建模为一个概率生成过程，并提出了一个称为EPIFNP的函数神经过程模型，该模型直接对预测值的概率密度进行建模。EPIFNP利用动态随机相关图以非参数的方式对序列之间的相关性进行建模，并设计不同的随机潜变量从不同的角度捕捉函数的不确定性。我们在实时流感预测环境中的大量实验表明，EPIFNP在精度和校准指标上都显著优于以前的最先进模型，精度高达2.5倍，校准性能高达2.4倍。此外，由于其生成过程的特性，EPIFNP学习当前季节与历史季节的类似模式之间的关系，从而能够进行可解释的预报。除了流行预测之外，EPIFNP对于推进用于预测分析的深度序列模型中的原则性不确定性量化具有独立的意义
Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations; e.g. it is difficult to specify meaningful priors in Bayesian NNs, while methods like deep ensembling are computationally expensive in practice. In this paper, we fill this important gap. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value. EPIFNP leverages a dynamic stochastic correlation graph to model the correlations between sequences in a non-parametric way, and designs different stochastic latent variables to capture functional uncertainty from different perspectives. Our extensive experiments in a real-time flu forecasting setting show that EPIFNP significantly outperforms previous state-of-the-art models in both accuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in calibration. Additionally, due to properties of its generative process,EPIFNP learns the relations between the current season and similar patterns of historical seasons,enabling interpretable forecasts. Beyond epidemic forecasting, the EPIFNP can be of independent interest for advancing principled uncertainty quantification in deep sequential models for predictive analytics