Discuss the advantages and limitations of using Recurrent Neural Networks (RNNs) for time series forecasting.
Instruction: Provide an overview of how RNNs can be applied to time series forecasting, including their strengths and weaknesses compared to traditional methods.
Updated
Example Answer
An RNN updates a hidden state as it processes a sequence, which can represent nonlinear relationships over time. It can combine lagged target information with other inputs, and gated variants such as LSTMs can help learn longer dependencies.
That does not mean traditional forecasting models ignore temporal dependence: ARIMA explicitly models dependence on earlier values and innovations. I would compare the RNN with those baselines at the same forecast horizons and information cutoffs. RNNs can require more data, tuning, and compute, and vanilla recurrent networks can have difficult gradient behavior over long sequences. I would check leakage in window construction and scaling, performance by series, and the cost of maintaining the model. Complexity needs an observed benefit.
Make it your own
Choose a nonlinear pattern the network might capture and a simpler baseline to challenge it.
Why this works
It compares modeling choices without inventing results or misrepresenting the baseline.
Follow-up to practice
How would you prevent leakage between overlapping windows?