Time Series Forecasting with Stacked Long Short-Term Memory Networks
arXiv:2011.00697v17 citations
AI Analysis
This work addresses traffic planning for urban management, but it is incremental as it builds on existing LSTM methods.
The paper tackled traffic volume forecasting by applying stacked LSTM networks, achieving improved accuracy in time series prediction.
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.