论文标题
基于概率增强LSTM神经网络的极端自适应时间序列预测模型
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks
论文作者
论文摘要
具有极端事件的预测时间序列是一个具有挑战性且普遍的研究主题，尤其是当时间序列数据受到复杂的不确定因素的影响时，例如水文预测中的情况。已应用多种传统和深度学习模型来发现非线性关系并识别这些类型的数据中的复杂模式。但是，现有方法通常忽略数据不平衡的数据或严重事件对模型培训的负面影响。此外，通常在少数一般行为良好的时间序列上评估方法，这并未显示其概括能力。为了解决这些问题，我们提出了一种新颖的概率增强神经网络模型，称为NEC+，该模型同时学习了极端和正常的预测功能，并通过选择性的背部传播来选择它们。我们评估了提出的模型，该模型适用于加利福尼亚州9个水库的3天的艰难的3天小时水位预测任务。实验结果表明，所提出的模型显着胜过最先进的基线，并在具有不同分布的数据上具有出色的概括能力。
Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.
