【时序模型】《Deep and Confident Prediction for Time Series at Uber》论文阅读
摘要 经典的时间序列模型常与不确定性估计的概率公式结合使用。然而这类模型很难调优、缩放和添加外生变量。针对近年来长短期记忆网络（LSTM）的兴起。该文提出了一种新的端到端贝叶斯深度度模型，该模型能够提供时间徐略预测和不确定性估计。 准确的时间序列预测具有挑战性的原因在于：1.差异较大的时段。2.因为极端事件预测依赖于许多外部因素，对于Uber，包括天气、城市人口增长或营销变化。这些外部变量很难纳入
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