论文标题
时间序列预测与堆叠的长短期内存网络
Time Series Forecasting with Stacked Long Short-Term Memory Networks
论文作者
论文摘要
长期记忆（LSTM）网络通常用于捕获时间依赖模式。通过堆叠多层LSTM网络，它可以捕获更复杂的模式。本文探讨了在时间序列预测域中应用堆叠的LSTM网络的有效性，特别是交通量预测。能够更准确地预测流量量可以改善计划，从而大大降低运营成本并提高整体效率。
Long Short-Term Memory (LSTM) networks are often used to capture temporal dependency patterns. By stacking multi-layer LSTM networks, it can capture even more complex patterns. This paper explores the effectiveness of applying stacked LSTM networks in the time series prediction domain, specifically, the traffic volume forecasting. Being able to predict traffic volume more accurately can result in better planning, thus greatly reduce the operation cost and improve overall efficiency.
