Time Series Forecasting with Stacked Long Short-Term Memory Networks
Operations Group（运营组）；Toronto Transit Commission（多伦多交通委员会）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
