LSTM vs GRU for Time Series

In this chapter, we compare the performance of LSTM (Long Short-Term Memory) networks and GRU (Gated Recurrent Units) on the same time series forecasting task used in Chapter 4, where we predicted stock prices. Both LSTM and GRU are popular variants of Recurrent Neural Networks (RNNs), but they have distinct architectures and behaviors that can influence their performance depending on the task.

Comparison Methodology: In this chapter, we use the same stock price prediction task as in Chapter 4, where we trained both an LSTM and a GRU model using the same dataset. Both models were trained using the same loss function (Mean Squared Error - MSE), optimizer (Adam), and evaluation metric (Root Mean Squared Error - RMSE).

Results and Insights:

• LSTM tends to perform better when the task requires capturing long-term dependencies, as its architecture is designed specifically for this purpose;
• GRU, being simpler and faster, can still achieve similar performance for tasks that don't require as much complexity or when training speed is crucial;
• For our stock price prediction task, both models performed well, but the LSTM had a slight edge in terms of prediction accuracy, especially for longer time sequences.

In summary, both LSTM and GRU are powerful tools for time series forecasting. The choice between them depends on the specific requirements of the task, including the complexity of the data and the need for computational efficiency. LSTM is ideal for tasks requiring complex long-term dependencies, while GRU offers a simpler, faster alternative with comparable performance.