论文标题
使用LSTMS预测可沉淀的水蒸气
Forecasting Precipitable Water Vapor Using LSTMs
论文作者
论文摘要
近年来，由于其在不同时期内学习模式的能力，近年来的时间序列预测已广泛使用长期内存（LSTM）网络。在本文中，该能力适用于学习全球定位系统（GPS）基于4小时的基于基于沉淀的水蒸气（PWV）测量的模式。在1500小时的记录数据中评估了训练有素的模型。它在未来的预测间隔为5分钟的均方根误差（RMSE）为0.098毫米，并且超过了较幼稚的方法，最高为40分钟。
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
