What are the key differences between LSTM and GRU when applying them to time series forecasting

Question

Can i know What are the key differences between LSTM and GRU when applying them to time series forecasting?

Answer 1

LSTM (Long Short-Term Memory) has separate memory and gating mechanisms, making it better for capturing long-term dependencies, whereas GRU (Gated Recurrent Unit) is computationally efficient with fewer parameters, making it faster but sometimes less precise in complex patterns.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Synthetic Data Generation: Uses a sinusoidal function with noise to simulate a time series dataset.
• Data Preparation: Converts the series into a supervised learning problem using a sliding window approach.
• Model Architectures: Implements both LSTM and GRU models with 50 units and ReLU activation.
• Training and Evaluation: Models are trained using the Adam optimizer and mean squared error (MSE) loss.
• Comparison Visualization: Plots actual vs. predicted values for both models to compare performance.

Hence, LSTM captures long-term dependencies better due to its additional memory cell, while GRU is computationally efficient and provides comparable performance with fewer parameters.