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
翻译:准确和可信赖的流行病预测是一个重要问题,对公共卫生规划和疾病缓解产生影响。大多数现有流行病预测模型忽视了不确定性量化,导致错误的预测。最近,在深神经模型中,为不确定意识-有时间序列预测而进行的近期工程也存在若干局限性;例如,很难在巴耶西亚NPs中指定有意义的前期,而深层混合等方法在实践中计算成本很高。在本文中,我们填补了这一重要的缺口。我们把预测任务模拟为一个稳定化的基因化过程,并提议一个功能性神经过程模型,称为EPIFNP,直接模拟预测值的概率密度。 EPIFNP最近对以非参数方式模拟序列之间的相关性作了动态的随机相关图表,并设计了不同的随机潜在变量,以从不同角度获取功能不确定性。我们在实时流感预测中进行的广泛实验表明,EPIFNP在准确性和校准度指标的精确度和校准度指标中都明显超前几个最新模型,在精确性和精确度上直接模拟预测。在IMFIMF的精度-IMF的准确性和2.4期预测中,在对当前历史周期的精确和2.4的精确的预测中,可以学习。