Recurrent Neural Networks for Time Series Forecasting
This work addresses forecasting difficulties for practitioners in fields like finance or weather prediction, but it is incremental as it builds on existing RNN methods without introducing major innovations.
The authors tackled the challenge of time series forecasting by developing a recurrent neural network framework that includes feature engineering, importance analysis, and prediction evaluation, and they empirically tested it using LSTM and GRU networks.
Time series forecasting is difficult. It is difficult even for recurrent neural networks with their inherent ability to learn sequentiality. This article presents a recurrent neural network based time series forecasting framework covering feature engineering, feature importances, point and interval predictions, and forecast evaluation. The description of the method is followed by an empirical study using both LSTM and GRU networks.